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Revision as of 17:31, 31 August 2020
| Faculty | Number | Title | Term | Instructor | Schedule(Lecture only) | Description | |
|---|---|---|---|---|---|---|---|
| ALES | AREC 513 | Econometric Applications | Fall 2020 | Feng Qiu | fq@ualberta.ca | TR 14:00-15:20 | Econometric theory, multiple linear regression analysis and interpretation, simultaneous equation estimation, qualitative choice models, time series analysis, applications of econometric techniques to resource and agricultural economic problems. Prerequisite: Intermediate course in statistics or econometrics. |
| ALES | AREC 565 | Economic Valuations of Ecosystem Services | Winter 2021 | Vic Adamowicz | adamowic@ualberta.ca | Economic valuation of ecosystem goods and services. Topics include: Theoretical and empirical analysis of environmental valuation methods, advanced benefit cost analysis, welfare economics, valuation of ecosystem goods and services, valuation of health impacts from environmental quality change, and linkages to experimental and behavioural economics. Prerequisite: *3 Introductory Econometrics course and consent of instructor; AREC 502 recommended. [Resource Economics and Environmental Sociology] | |
| Arts | C LIT 210 | Cyberliterature | Winter 2021 | Florian Mundhenke | mundhenk@ualberta.ca | MWF 13:00-13:50 | An introduction to the relations between literature and online textuality. |
| Arts | DH 500 | Survey of Digital Humanities | Fall 2020 | Jonathan Cohn | cohn@ualberta.ca | M 9:00-11:50 | This course will provide students with an overview of the discipline of Digital Humanities and its varied applications across a range of disciplines and domains of knowledge. The course is designed to enable students to situate their own research interests within the broader framework of Digital Humanities and to make informed choices about how they structure the rest of their program. The course is divided into three key areas: 1) debates, theories, and key concepts; 2) emblematic projects and organizations; and 3) tools of the trade. Upon completion of the course, students will be able to situate their own research interests within the larger context of the field, evaluate existing methodologies and projects, consider the ability of computer systems to represent knowledge, and analyze the impact of technology on cultural production. |
| Arts | DH 510 | Contemporary Media Theory | Fall 2020 | Michael Litwack | litwack@ualberta.ca | W 13:00-15:50 | |
| Arts | DH 510 | Information Ethics | Winter 2021 | Geoffrey Rockwell | grockwel@ualberta.ca | M 13:00-15:50 | The ethical use of information has become increasingly important in this age of social media. This course will ask what information is, discuss current issues in data ethics, look at codes of ethics, and introduce selected frameworks like the ethics of care that are used to help decision making. Students will be asked to develop case studies, to present theories in class, and to apply ethical theory to cases. |
| Arts | DH 530 / GSJ 598 | Data, Power, Feminism | Winter 2021 | Deb Verhoeven | debver@ualberta.ca | T 13:00-15:50 | |
| Arts | ECON 403 | Economic Data Analysis | Winter 2021 | Xingfei Liu/ Jiatong Zhong/ Max Sties | xingfei@ualberta.ca/ jzhong5@ualberta.ca/ sties@ualberta.ca | TR 14:00-15:20 |
The course uses the programming languages R, SAS, and Python to perform analysis of economics data. Prerequisite: ECON 299 or equivalent, and ECON 109. |
| Arts | HIST 115 | Technology and History | Fall 2020 / Winter 2021 | Lech Lebiedowski | lech@ualberta.ca | MWF 11:00-11:50 or T 18:00-21:00 (Winter only) | This course examines the development of various technologies and their impact on the course of history. The course objectives are: 1) to trace the origins of our modern technological society; 2) introduce students to critical thinking and recent historiography; 3) learn key concepts, developments, and events in the history of technology. |
| Arts | LING 520 | Computational Linguistics | Fall 2020 | Antti Arppe | arppe@ualberta.ca | T 9:00-11:50 | Theoretical and implementation aspects of: computational morphology and phonology, part-of-speech tagging, parsing, grammar engineering, lexical semantics, and corpus analysis. Prerequisites: LING 308, 209 and 310; or Consent of Department. |
| Arts | LING 603 | Quantitative Methods in Linguistics | Winter 2021 | Antti Arppe | arppe@ualberta.ca | T 9:00-11:50 | Multivariate statistical methods as applied to linguistic data, and other statistical techniques of interest to linguists. Prerequisite: LING 523 or consent of the Department. |
| Arts | MUSIC 445 | Electroacoustic Music | Winter 2021 | Scott Smallwood / Mark Hannesson | ssmallwo@ualberta.ca / mjh7@ualberta.ca | TR 11:00-11:20 | Electroacoustic music techniques, history and repertoire. Prerequisite: consent of department. Registration priority will be given to BMus (all routes), BA (Honors) Music Major, BEd Music Major/Minor, BA Music Major and graduate students in Music. |
| Arts | MUSIC 545 | Interactive Sounds and Systems | Fall 2020 | Scott Smallwood / Mark Hannesson | ssmallwo@ualberta.ca / mjh7@ualberta.ca | TR 15:30-16:50 | Seminar in the design and application of interactive musical systems using media languages such as Max/MSP or equivalent. Prerequisites: Music 445 or equivalent or consent of Department. |
| Arts | PHIL 205 | Philosophy of Mind | Winter 2021 | Tugba Yoldas | yoldas@ualberta.ca | MWF 15:00-15:50 | An introduction to some basic questions in the philosophy of mind such as the relation between bodies and minds, the nature of consciousness, the ontological status of mental states, agency and personhood, artificial intelligence, and mental causation in relation to free will. |
| Arts | PHIL 365 | Philosophy of Computing | Fall 2020 | Katalin Bimbo | bimbo@ualberta.ca | MWF 12:00-12:50 | Computers, computing and information sciences are ubiquitous in their presence and impact on every aspect of our lives. The course will introduce certain notions of computing and delineate the limit of what is computable. Some related intriguing questions concern information security, quantum computing and human-computer interaction. A particularly popular philosophical question is about the relationship between human intelligence and artificial intelligence, especially, in the light of AI's development in the last 70 years or so. Computer science combines characteristics of empirical and theoretical sciences. The course will also consider where to place computer science in the realm of sciences. |
| Arts | PHIL 366 | Computers and Culture | Fall 2020 | Richard Kover | kover@ualberta.ca | TR 12:30-13:50 | This course examines the psychological, cultural, socioeconomic and political dimensions of the information revolution, and more particularly of social media. Some of the questions to be discussed include: What is social about social media? How is subjectivity, identity and community constructed and expressed online, and how does this differ from offline forms? Is crowd-sourcing a key to reinvigorating participatory democracy or just another word for groupthink? Are “presumption” and “playbour” new forms of economic exploitation? Does social media provide a template for a truly emancipatory politics or new forms of domination? Take this course if you’re interested in the social and political transformations brought about by the internet and social media! |
| Arts | PHIL 384 | Ethics and Artificial Intelligence | Winter 2021 | Howard Nye | hnye@ualberta.ca | MWF 14:00-14:50 | Artificial intelligence systems are revolutionizing our world, and raising many difficult ethical issues that we will explore in this course. When AI systems are used to make important decisions in such areas as medicine, employment, and the criminal justice system, how can we tell if they are discriminatory, and how can we address the fairness of these decisions? Who bears responsibility for the outcomes of AI systems, and how can we responsibly draw upon their strengths while protecting ourselves from their weaknesses? How can we address the profound impacts that AI systems are having upon our social relationships, privacy, employment and economic power relations, and political freedoms? How can we responsibly govern the use of AI systems, ensure that these systems are used in ethical ways, and prevent governments and corporations from using AI systems to wield undue power? To what extent must we worry about the possibility that in the near future AI systems themselves may become moral patients to whom we owe duties, and the existential risks of creating extremely powerful AI systems that we cannot control? Take this course if you are interested in the pressing ethical problems posed by the ways in which AI systems are radically changing our world |
| Arts | STS 200 | Introduction to Science, Technology, and Society | Fall 2020 / Winter 2021 | Lech Lebiedowski / Nathan Kowalski | lech@ualberta.ca | W 17:00-20:00 ; TR 11:00-12:20; TR 9:30-10:50 (Winter only) | An examination of the interrelations of science, technology, society and environment, emphasizing an interdisciplinary humanities and social sciences perspective. Note: not to be taken by students with credit in INT D 200. |
| Arts | STS 351 | Understanding Video Games | Fall 2020 | Sean Gouglas | sgouglas@ualberta.ca | Beginning with an exploration of games in general and leading to modern video games. This course will be delivered on-line and is offered in a Cost Recovery format at an increased rate of fee assessment; refer to the Fees Payment Guide in the University Regulations and Information for Students section of the Calendar. Not open to students with credit in or enrolled in STS 350. | |
| Business | BUEC 488 | Data Science for Business Economics | Winter 2021 | Max Sties | sties@ualberta.ca | MWF 15:00-15:50 | This course provides an applied treatment of selected topics in supervised and unsupervised machine learning with emphasis on decision making within firms and markets. Normally restricted to third- and fourth-year Business students. Prerequisites: BUEC 311 or ECON 281, or consent of Department. Additional prerequisites may be required. |
| Business | FIN 412 | Investment principles | Fall 2020 / Winter 2021 | Fall: Akiko Watanabe / Sahil Raina; Winter: Runjing Lu | fujimoto@ualberta.ca / sraina@ualberta.ca; runjing1@ualberta.ca | TR 12:30-13:50; TR 14:00-15:20; TR 15:30-16:50 | This course examines securities and securities markets with emphasis on stocks and bonds. Topics include information, interest rates, risk-return relationships, efficient markets, diversification, portfolio performance measurement, and the application of financial theory to investment decisions. Prerequisite: FIN 301 and MGTSC 312. Students may not receive credit for both FIN 412 and ECON 442. |
| Business | FIN 488 | Applied Data Science in Finance | Fall 2020 / Winter 2021 | Philippe Cote | pcote@ualberta.ca | W 9:30-12:20; W 14:00-16:50 | Data scientists are increasingly sought after in the market place. It is a field requiring a vast range of skills as it encompasses multiple disciplines. The core objectives of this class are to: (1) Provide you with a DS toolkit to implement concepts and theory; (2) Gain an ability to navigate with confidence from identifying a business need to providing or contributing to a solution; (3) Articulate your solution(s) with effective visualization supporting business communication and discussions. Normally restricted to third- and fourth-year Business students. Prerequisites: FIN 301 or consent of Department. Additional prerequisites may be required. |
| Business | MGTSC 405 | Forecasting for Planner and Managers | Fall 2020 / Winter 2021 | Fall: Ivor Cribben / Winter: Reidar Hagtvedt | cribben@ualberta.ca; hagtvedt@ualberta.ca | Fall: T 14:00-15:20, R 14:00-15:20; Winter: T 9:30-10:50, TR 9:30-10:50 | This course is concerned with methods used to predict the uncertain nature of business trends in an effort to help managers make better decisions and plans. Such efforts often involve the study of historical data and manipulation of these data to search for patterns that can be effectively extrapolated to produce forecasts. This is a business statistics course that covers all aspects of business forecasting where the emphasis is on intuitive concepts and applications. Topics covered include the family of exponential smoothing methods, decomposition methods, dynamic regression methods, Box-Jenkins methods and judgmental forecasting methods (e.g. the Delphi method). Because forecasting is best taught through practice, the course contains numerous real, relevant, business oriented case studies and examples that students can use to practice the application of concepts. Prerequisites: MGTSC 312, MGTSC 352 or OM 352. |
| Business | MGTSC 488/645 | Introduction to Business Analytics | Winter 2021 | Reidar Hagtvedt | hagtvedt@ualberta.ca | MW 18:00-21:00 | The course is intended to provide an overview of exactly this area, if you define it as data science and not solely AI. The main method that would be considered pure AI is neural networks, and the course spends several weeks on this that. The merging of massive data-sets with analytical tools from Statistics, Computer Science, and Operations Research has created the emerging field of analytics. Methods are developing rapidly based on statistical platforms such as SAS and R, or more general purpose programming tools such as Python. This course will build on the basis from MGTSC 501 to provide an overview of Big Data and analytics, and develop programming and methodological skills to acquire, analyze, and present analysis |
| Business | MGTSC 488/705 | Multivariate Data Analysis I | Fall 2020 | Ivor Cribben | cribben@ualberta.ca | W 9:00-11:50 | An overview of multivariate data analysis normally taken by students in the first year of the Business PhD program. Designed to bring students to the point where they are comfortable with commonly used data analysis techniques available in most statistical software packages. Students are expected to complete exercises in data analysis and in solving proofs of the major results. Topics will include univariate analysis, bivariate analysis, multiple linear regression, and analysis of variance. It is expected that students have as background at least one semester of calculus, one semester of linear algebra, and two semesters introduction to probability, probability distributions and statistical inference. Prerequisite: Registration in Business PhD Program or written permission of instructor. |
| Business | MGTSC 501 | Data Modeling | Fall 2020 | Reidar Hagtvedt | hagtvedt@ualberta.ca | T 9:00-11:50, T 18:30-21:30, R 18:30-21:30 | This course begins with a survey of graphical and numerical techniques available for studying and describing data. Following an introduction to probability distributions, an overview of statistical inference for means and proportions is provided. Regression, analysis of variance and decision analysis are then utilized to analyze data and support decision making. Time series models are also briefly discussed. The data and decisions analyzed throughout the course will be representative of those commonly encountered by managers. During the required lab sessions, spreadsheet analysis of data, Monte Carlo simulation and the use of software for statistical analysis will be presented. Not open to students who have completed MGTSC 511 and MGTSC 521. |
| Business | MIS 311 | Management Information Systems | Fall 2020 / Winter 2021 | Robb Sombach / Jim Kiddoo | sombach@ualberta.ca; jkiddoo@ualberta.ca | Fall: T 18:30-21:30, TR 8:00-9:20, TR 15:30-16:50; Winter: T 18:30-21:30, TR 8:00-9:20, TR 9:30-10:50, TR 14:00-15:20 | Introduction to all major areas of information systems. Technology and file systems, organizational and behavioral issues, datamodeling, databases, expert systems, systems analysis, systems development life cycle, etc. Development of analytical skills which can be brought to bear on MIS problems. Notes: Students are expected to have basic familiarity with microcomputer applications (word processing, spreadsheets, personal data base, presentation graphics, personal information manager, email, web browser). |
| Business | OM 352 | Operations Management | Fall 2020 | Fall: Saied Samiedaluie; Winter: Armann Ingolfsson | Fall: samiedal@ualberta.ca; Winter: aingolfs@ualberta.ca | Fall: TR 12:30-13:50; Winter: TR 11:00-12:20, TR 15:30-16:50 | A problem-solving course which introduces the student to deterministic and stochastic models which are useful for production planning and operations management in business and government. Note: Students are expected to have basic familiarity with microcomputer applications. Prerequisite: MATH 114 or equivalent and STAT 151 or equivalent. |
| Business | OM 420/620 | Predictive Business Analytics | Fall 2020 / Winter 2021 | Fall: Ilbin Lee; Winter: Mohamad Soltani | Fall: ilbin@ualberta.ca ; Winter: soltani@ualberta.ca | MW 11:00-12:20, MW 14:00-15:20 (Fall only) | Application of predictive statistical models in areas such as insurance risk management, credit risk evaluation, targeted advertising, appointment scheduling, hotel and airline overbooking, and fraud detection. Students will learn how to extract data from relational databases, prepare the data for analysis, and build basic predictive models using data mining software. Emphasizes the practical use of analytical tools to improve decisions rather than algorithm details. Prerequisite: MGTSC 352 or OM 352. |
| Business | SMO 330 | Introduction to Entrepreneurship | Fall 2020 | Tim Hannigan | thanniga@ualberta.ca | MW 15:30-16:50 | includes a substantial component on the use of analytics and AI systems; This is an interdisciplinary course for students interested in developing an idea for a new product or service into a market reality and an investable story. This course is about developing the analytical and conceptual skills required to assess the potential for a new venture. Working on a team composed of students from across different faculties, students will generate an idea, use business modeling techniques to "flesh out" that idea and define a venture opportunity, move through the customer research and development process in order to assess how to improve their new venture concept, and "pitch" their idea. Topics covered in this course will include: idea generation, business-model development, market definition, customer discovery, competitive analysis, and resource development. Open to students in any Faculty with the consent of the Department. Not open to students in first year. |
| Business | SMO 441 | Strategy and Innovation | Fall 2020 / Winter 2021 | Fall: David Deephouse ; Winter: Christopher Steele | Fall: davidd@ualberta.ca ; Winter: csteele1@ualberta.ca | Fall: TR 12:30-13:50, TR 14:00-15:20 (Winter only) | includes a section devoted to the impact of the contemporary data landscape on strategy-making, mapping different types of business analytics, and sketching the challenges of implementing analytic solutions.; This course examines top management decisions and emphasizes the development of business and corporate strategy. It integrates the management principles studied in the business core using a series of business cases. The course will have a special focus on innovation and innovative ways of competing and creating value. Guest Faculty members and executives will participate. Prerequisites: FIN 301; MARK 301; and SMO 201, 301 or 310. Open only to students in the Faculty of Business. |
| Business | SMO 488 | Management Analytics | Winter 2021 | Dev Jennings | dj1@ualberta.ca | T 13:00-14:20, T 14:30-15:50 | This class will help students understand how to use data and algorithms to generate organizational value. Students will learn about the two major perspectives on analytics: (1) a positive perspective that highlights the potential for organizations to use analytics, data, and artificial intelligence to generate extreme value, and (2) a negative perspective that highlights the ways that analytics, data, and artificial intelligence can lead to abuses of power and undermine ethics. After learning about these two perspectives, students will discover how organizations can leverage analytical tools and techniques to enhance organizational decision‐making through the application of a novel conceptual framework, The Biography of an Algorithm, in a variety of empirical contexts including the security industry, the ready mix concrete industry, and the display advertising industry. The class will be designed to be an integrative and interdisciplinary course. Rather than teach students how to do analytics, this course focuses on applying analytics to a variety of real world business problems. Specifically, this course takes a general management perspective that emphasizes the role of individuals and organizations using analytics to make strategic decisions and embark upon courses of strategic action. The course is aimed at helping to develop the “general management point of view” among participants. This point of view is the best vantage point for making decisions that affect long run business performance. Prerequisites: SMO 201, 301 or 310 or consent of Department. Additional prerequisites may be required. |
| Business | SMO 502 | Organization Strategy-Managing Organizations | Winter 2021 | Dev Jennings | dj1@ualberta.ca | W 18:30-21:30 | The first part of this course examines the formation of business strategy. It recognizes the complexities and messiness of strategy formation and explores how organizations actually develop strategies. The second part examines the evolution, determinants, and relevance of alternative ways of organizing. Contemporary ideas (e.g. re-engineering, the learning organization, virtual organizations) are critically reviewed. Not open to students who have completed SMO 610. Prerequisite: SMO 500. |
| Business | SMO 641 | Business Strategy | Fall 2020 / Winter 2021 | Anthony Briggs | abriggs@ualberta.ca | R 18:30-21:30, R 14:00-16:50 (Winter only) | This course examines top management decisions and emphasizes the development of business and corporate strategy. It integrates the management principles studied in the business core using a series of business cases. Guest Faculty members and executives will participate. Prerequisite: All required Year one MBA core courses. |
| Education | EDPY 597 A02 | Machine Learning Theory and Applications | Fall 2020 | Maria Cutumisu | cutumisu@ualberta.ca | F 9:00-11:50 | |
| Education | EDPY 597 B02 | Educational Data Mining | Winter 2021 | Maria Cutumisu | cutumisu@ualberta.ca | F 13:00-15:50 | |
| Education | EDU 210 | Introduction to Educational Technology | Fall 2020 / Winter 2021 | Fall: Catherine Adams |
Fall: caadams@ualberta.ca |
T 17:00-18:30 | This course examines frameworks, trends, issues and futuristic scenarios on the role of technology in education. Students will gain hands-on experience of using technology, with a special emphasis on strategies for integrating technology into the school curriculum. Students may not receive credit for both EDU 210 and EDIT 202. Prerequisite: EDU 100 or pre/corequisite EDU 300 (After Degree students). May contain alternative delivery sections; refer to the Fees Payment Guide in the University Regulations and Information for Students section of the Calendar. |
| Education | LIS 538 | Digital Librairies | Winter 2021 | Ali Shiri / Brenda Reyes Ayala | ashiri@ualberta.ca; reyesaya@ualberta.ca | M 9:00-11:50 | An introduction to the concept, development, types and trends of digital libraries. This course will focus on the creation, organization, access, use and evaluation of digital libraries with a view to socioeconomic and cultural issues. Sections may be offered in a Cost Recovery format at an increased rate of fee assessment; refer to the Fees Payment Guide in the University Regulations and Information for Students. Prerequisites: LIS 501, 502, 503, and 505, or consent of instructor. |
| Engineering | CIV E 295 | Civil Engineering Analysis II | Winter 2021 | Yong Li | yong9@ualberta.ca | MWF 11:00-11:50 | Application of numerical methods to civil engineering problems. This course includes a chapter on artificial intelligence and machine learning. Prerequisites: ENCMP 100 and MATH 102. |
| Engineering | CIV E 603 | Construction Informatics | Fall 2020 | Yasser Mohamed | yaly@ualberta.ca | R 13:00-15:50 | Computer-aided information management in construction, including relational database development and management, application of data mining techniques, computer programming, and application of computers in the planning, organization and control of construction projects. |
| Engineering | CIV E 654 | Artificial Intelligence and Automation in Construction | Winter 2021 | Aminah Robinson Fayek | aminah@ualberta.ca | T 9:30-12:20 | Prototyping techniques applied to the design and development of systems based on artificial intelligence techniques for use in construction. |
| Engineering | CME 694 X09 | Advanced Process Data Analytics | Fall 2020 | Vinay Prasad | vprasad@ualberta.ca | T 17:00-20:00 | The course explores machine learning and data analytic techniques of interest to engineers in general and chemical and process engineers in particular. Industrially relevant examples and case studies are discussed. Techniques suited to stationary and dynamic data are explored. |
| Engineering | ECE 624 | Fuzzy Set in Human Centric Computing | Fall 2020 | Witold Pedrycz | wpedrycz@ualberta.ca | TR 9:30-10:50 | Developments in human-centric systems. Fuzzy sets and information granulation. Computing with fuzzy sets: logic operators, mapping, fuzzy relational calculus. Fuzzy models and rule-based models. Fuzzy neural networks. Fuzzy clustering and unsupervised learning. |
| Engineering | ECE 626 | Advanced Neural Networks | Winter 2021 | Scott Dick | sdick@ualberta.ca | MWF 13:00-13:50 | Introductory and advanced topics in neural networks and connectionist systems. Fast backpropagation techniques including Levenberg-Marquardt and conjugate-gradient algorithms. Regularization theory. Information-theoretic learning, statistical learning, dynamic programming, neurodynamics, complex-valued neural networks. |
| Engineering | ECE 627 | Intelligent Web | Winter 2021 | Marek Reformat | reformat@ualberta.ca | MWF 12:00-12:50 | Representation, processing, and application of knowledge in emerging concepts of Semantic Web: ontology, ontology construction, and ontology integration; propositional, predicate and description logics; rules and reasoning; Semantic Web services; Folksonomy and Social Web; Semantic Web applications. |
| Engineering | ECE 720 A3 | Metaheuristic Optimization | Fall 2020 | Petr Musielk | pmusilek@ualberta.ca | MWF 10:00-10:50 | |
| Engineering | ECE 720 A4 | Data Analytics for Software Engineering | Fall 2020 | Cor-Paul Bezemer | bezemer@ualberta.ca | TR 14:00-15:20 | |
| Engineering | MEC E 614 | Iterative Learning Control | Winter 2021 | Bob Koch | ckoch@ualberta.ca | TR 11:00-12:20 | Mathematical preliminaries (discrete time systems). Stability and transient response of Iterative Learning Control (ILC). Design of ILC in both the time and frequency domain. Convergence and design of repetitive control. |
| Engineering | MIN E 422 | Environmental Impact of Mining Activities | Winter 2021 | Jeffery Boisvert | jbb@ualberta.ca | WF 12:00-12:50 | Environmental impact of mining projects and activities. Topics include: environmental impact assessment (EIA) processes, sustainable development, mine closure, reclamation planning, social responsibility of mining, regulations, guidelines, surface subsidence, tailings disposal, erosion and acid rock drainage. Corequisite: MIN E 413. |
| Engineering | PET E 477 | Modelling in Petroleum Engineering | Winter 2021 | Juliana Leung | juliana2@ualberta.ca | MWF 12:00-12:50 | This course covers the basics of numerical reservoir simulation and numerical solution of partial differential equations, as well as simulation methods as applied to specific problems in subsurface flow behavior. In addition, commercial simulation packages are used to model primary, secondary, and tertiary recovery phases of petroleum production. It also discusses how machine learning can be integrated with numerical simulation for various subsurface engineering design and optimization applications. Prerequisites: PET E 373 and CH E 374. |
| Law | LAW 589-A05 | Coding the Law | Fall 2020 | Jason Morris | jmorris@ualberta.ca | F 9:00-11:50 | In the future lawyers will be called upon to advise their clients and their practices in developing, or certifying, or objecting to the outcomes of automated legal services. Lawyers who are aware of the landscape of possibilities, and who have experience and gain confidence learning to use these new technologies, will be better equipped to protect and serve their clients and the public.This course will not provide the law student with experience using the technologies they are most likely to be exposed to in practice. Instead, this course is designed to give law students an introduction to the types of technologies being used for automating legal services now and in the near future, their strengths, their weaknesses, and the trends in their development going into the future. The Course has three primary objectives: (1) Give students practical experience automating legal services with current technology; (2) Give students a survey of and experience applying best practices in the automation of legal services; (3) Introduce students to an array of technologies for automating legal services. The course will provide students with an introduction to some of the best practices in automating legal services with regard to issues include: (1) Regulatory compliance, and issues of professional liability for automated legal services; (2) Ethical issues, such as algorithmic bias; (3) Administrative law issues such as transparency and appealability; (4) Privacy issues and the use of and limits of encryption technologies; (5) Professionalism issues such as informed consent as to the risks of automated services. Most exciting, the course will provide law students with the opportunity and skills to design and implement, in collaboration with a real or simulated client organization, an automated legal service using user-friendly, modern, free, open-source technology. |
| Law | LAW 599-A07 | Digital Law | Fall 2020 | Péter Szigeti | szigeti@ualberta.ca | TR 15:30-16:50 | |
| Medicine and Dentistry | PSYCI 511 | Biological Aspects of Psychiatry | Winter 2021 | Esther Fujiwara | efujiwara@ualberta.ca | TR 8:00-9:200 | Lectures and seminars on: classification, description and measurement of psychiatric disorders; sleep disorders; biochemical theories of psychiatric disorders, and discussions of how the actions of the drugs used to treat these disorders relate to these theories; practical aspects of drug treatment; biological markers; brain imaging; women's health issues; herbal products and psychiatry. Prerequisite: Permission of Department. |
| Medicine and Dentistry | RADDI 514 | Image Processing and Analysis in Diagnostic Imaging | Fall 2020 | Kumaradevan Punithakumar | punithak@ualberta.ca | TR 9:30-10:50 | The course aims to cover medical image processing and analysis techniques, including de-noising, registration, segmentation, 3D reconstruction, applicable in diagnostic imaging modalities such as ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI). Clinical examples in cardiovascular, musculoskeletal and brain imaging will be discussed. Prerequisite: Linear algebra and knowledge in MATLAB programming or consent of Department. |
| Rehabilitation Medicine | OCCTH 522 | Enabling Occupation Through the Use of Assistive Technology | Winter 2021 | Adriana Rios Rincon | aros@ualberta.ca | R 9:00-11:50 | Theory and practice skills to ensure the correct interface between clients' needs, assistive technology, occupation, and context. This course has some basic content on AI as it can be used with individuals with disabilities. A calendar change has been approved to increase the credit number from 3 to 4. Our plan is to significantly increase the AI content in the course. |
| Science | CMPUT 250 | Computers and Games | Fall 2020 / Winter 2021 | Nathan Sturtevant | nathanst@ualberta.ca | TR 11:00-12:20 | An interdisciplinary course for students in Science, Arts, and other faculties. The focus is on games as interactive entertainment, their role in society, and how they are made. Teams composed of students with diverse backgrounds (e.g. English, Art and Design, and Computing Science) follow the entire creative process: from concept, through pitch, to delivery, of a short narrative-based game using a commercial game engine. To achieve the required mix of backgrounds and experience, students must apply for admission to this course. Prerequisites: Second-year standing. |
| Science | CMPUT 296 A1/B1 | Basics of Machine Learning | Fall 2020 / Winter 2021 | Fall: James Wright; Winter: Martha White | Fall: jwright4@ualberta.ca; Winter: whitem@ualberta.ca | Fall: TR 12:30-13:50; Winter: TR 14:00-15:20 | The field of machine learning involves the development of statistical algorithms that can learn from data, and make predictions on data. These algorithms and concepts are used in a range of computing disciplines, including artificial intelligence, robotics, computer vision, natural language processing, data mining, information retrieval, bioinformatics, etc. This course introduces the fundamental statistical, mathematical, and computational concepts in analyzing data. The goal for this introductory course is to provide a solid foundation in the mathematics of machine learning, in preparation for more advanced machine learning concepts. The course focuses on univariate models, to simplify some of the mathematics and emphasize some of the underlying concepts in machine learning, including how should one think about data; how can data be summarized; how models can be estimated from data; what sound estimation principles look like; how generalization is achieved; and how to evaluate the performance of learned models. |
| Science | CMPUT 296 A2 | Game Artificial Intelligence | Fall 2020 | Matthew Guzdial | guzdial@ualberta.ca | MWF 11:00-11:50 | Game AI is distinct from "academic AI" in that the end behavior is the target. Game AI programmers are less concerned with the underlying algorithms and more so with the end result. For example, if having an AI ‘cheat’ provides a more entertaining experience, then cheating will likely be a main component of the design. There are also characteristics of many games that focus Game AI on specific problems, like navigation through a virtual world, tactics, and believable behavior. Academic AI researchers are more concerned with rational behavior, knowledge representations, robust multi-agent communication, etc. However, there are overlaps between the two domains, where the desired behavior requires less cheating and more realistic decision-making. This course will survey topics related to this overlap, with a focus on applying what we review in depth through implementations in digital games. This course also observes the difference between AI as a technical challenge for opposing forces AI in games and the integration of AI as a key aesthetic component of the gaming experience. Lectures and projects will explore both of these views of Game AI. |
| Science | CMPUT 325 | Non-Procedural Programming Languages | Winter 2021 | Jia-Huai You | jyou@ualberta.ca | TR 9:30-10:50 | A study of the theory, run-time structure, and implementation of selected non-procedural programming languages. Languages will be selected from the domains of functional, and logic-based languages. Prerequisites: CMPUT 201 and 204 or 275; one of CMPUT 229, E E 380 or ECE 212, and MATH 125. |
| Science | CMPUT 350 | Advanced Games Programming | Fall 2020 | Michael Buro | mburo@ualberta.ca | TR 11:00-12:20 | This course focuses on state-of-the-art AI and graphics programming for video games. Part 1 introduces C++, the language of choice for video game engines, emphasizing efficiency, safety, the Standard Template Library, and OpenGL. Part 2 on real time strategy deals with efficient pathfinding algorithms, planning, and scripting AI systems. Student projects give hands-on experience directly applicable to the video games industry. Prerequisite: CMPUT 201 or 275, and 204. May not be offered every year. |
| Science | CMPUT 361 | Introduction to Information Retrieval | Winter 2021 | Deilson Barbosa | denilson@ualberta.ca | TR 12:30-13:50 | Most of the knowledge we acquire, use, and share is expressed in natural language, and preserved as primarily textual documents. This course introduces the fundamental algorithms and data structures for organizing and searching through large collections of documents, and the techniques for evaluating the quality of search results. The course also covers practical machine-learning algorithms for text and foundational technologies used by Web search engines. Prerequisite: CMPUT 201 and CMPUT 204 or 275; MATH 125 or equivalent is strongly recommended. |
| Science | CMPUT 366 | Intelligent Systems | Fall 2020 / Winter 2021 | Fall: Vadim Bulitko; Winter: James Wright | bulitko@ualberta.ca ; jwright4@ualberta.ca | Fall: TR 14:00-15:20/TBD ; Winter: MWF 11:00-11:50/TBD | Introduction to artificial intelligence focusing on techniques for building intelligent software systems and agents. Topics include search and problem-solving techniques, knowledge representation and reasoning, reasoning and acting under uncertainty, machine learning and neural networks. Recent applications such as planning and scheduling, diagnosis, decision support systems, and data mining. Prerequisites: CMPUT 204 or 275; one of STAT 141, 151, 235 or 265 or SCI 151. |
| Science | CMPUT 397 | Reinforcement Learning | Fall 2020 / Winter 2021 | Fall: Martha White / Winter: Rupam Mahmood | Fall: whitem@ualberta.ca; Winter: ashique@ualberta.ca | MWF 13:00-13:50 | This course provides an introduction to reinforcement learning intelligence, which focuses on the study and design of agents that interact with a complex, uncertain world to achieve a goal. We will emphasize agents that can make near-optimal decisions in a timely manner with incomplete information and limited computational resources. The course will cover Markov decision processes, reinforcement learning, planning, and function approximation (online supervised learning). The course will take an information-processing approach to the concept of mind and briefly touch on perspectives from psychology, neuroscience, and philosophy. |
| Science | CMPUT 466/566 | Introduction to Machine Learning | Fall 2020 / Winter 2021 | Fall: Lili Mou; Winter: Alona Fyshe | Fall: lmou@ualberta.ca; Winter: alona@ualberta.ca | Fall: TR 12:30-13:50; Winter: TR 11:00-12:20 | Learning is essential for many real-world tasks, including recognition, diagnosis, forecasting and data-mining. This course covers a variety of learning scenarios (supervised, unsupervised and partially supervised), as well as foundational methods for regression, classification, dimensionality reduction and modeling. Techniques such as kernels, optimization and probabilistic graphical models will typically be introduced. It will also provide the formal foundations for understanding when learning is possible and practical. Prerequisite: one of CMPUT 340, 418 or equivalent knowledge; one of STAT 141, 151, 235 or 265 or SCI 151; or consent of Instructor. |
| Science | CMPUT 496/504 | Intelligent User Interfaces | Winter 2021 | Carrie Demmans Epp | demmanse@ualberta.ca | MW 14:00-15:20 | This course focuses on intelligent user interfaces (IUI), which is an area of computer science that combines artificial intelligence (AI) with human computer interaction. IUIs provide adaptive and personalized experiences to people using AI and machine learning techniques to make sense of the environment, the user, or the task. This adaptivity is meant to ensure that the system provides appropriate supports, options, or interactions to users. Topics will include adpatation approaches, user modelling, inferring user state, multi-modal interfaces, and approaches to evaluating IUIs. This is a project-based course where students will work in teams to develop and evaluate an IUI or its components. |
| Science | CMPUT 497/501 | Introduction to Natural Language Processing | Fall 2020 | Carrie Demmans Epp/ Denilson Barbosa/ Greg Kondrak | demmanse@ualberta.ca; denilson@ualberta.ca; gkondrak@ualberta.ca | TR 15:30-16:50 | Natural language processing (NLP) is a subfield of artificial intelligence concerned with the interactions between computers and human languages. This course is an introduction to NLP, with the emphasis on writing programs to process and analyze text corpora. The course covers both foundational aspects and applications of NLP. The course aims at a balance between classical and statistical methods for NLP, including methods based on machine learning. |
| Science | CMPUT 607 | Empirical Reinforcement Learning | Winter 2021 | Adam White | amw8@ualberta.ca | MW 12:30-13:50 | This course will focus on doing good experiments in reinforcement learning (RL). Reinforcement Learning is a fast growing field. Learning systems are becoming more complex and are routinely applied to complex games, 3D simulators, and robots. It is challenging to evaluate these systems because performance depends on carefully setting numerous hyper-parameters and each experiment may consume vast amounts of data and compute---sometimes running for days over even weeks on super clusters. It is not secret that many of the empirical results published in the RL literature are suspect or flat out misleading. This course will focus on designing and conducting good experiments in RL. We will survey best practices and criticism of popular methodologies used in the field. The objective of the course is to train each student to be a good RL empiricist---which will be demonstrated with a final project focused on conducting a good experiment. The class will be a mix of lecture, student presentations on papers from the literature, and the final project. |
| Science | CMPUT 609 | Reinforcement Learning II | Winter 2021 | Richard Sutton | rsutton@ualberta.ca | TR 15:30-16:50 | This course is an advanced treatment of the reinforcement learning approach to artificial intelligence, emphasizing the second and third parts of the second edition of the textbook Reinforcement Learning: An Introduction, by the instructor, Rich Sutton, and Andrew Barto. Students should have covered Part I of the textbook either in a previous course (such as CMPUT 366) or in extensive self study. Also required is comfort with the mathematics of probability distributions, expectations, linear algebra, and elementary calculus. |
| Science | CMPUT 644 | Intelligent and Connected Systems | Fall 2020 | Omid Ardakanian | oardakan@ualberta.ca | TR 14:00-15:20 | Sensors and control nodes with computing and communication capabilities are becoming integral parts of modern systems and structures. The network of these intelligent and connected devices, dubbed as the Internet of Things (IoT), has significant potential to enable various applications and presents new energy savings and revenue generation opportunities in different domains, from smart cities to healthcare and energy systems. This course covers several cross-cutting topics and important papers from the literature on IoT and Cyber-Physical Systems (CPS). These topics include sensor fusion, distributed control, energy and performance optimization, time synchronization, and privacy-preserving data analysis. |
| Science | CMPUT 651 | Deep Learning for NLP | Fall 2020 | Lili Mou | lmou@ualberta.ca | MW 9:00-10:20 | This course provides advanced topics in deep learning for natural language processing. In addition to traditional deep learning materials (CNNs, RNNs, etc.), the course pays more attention to structured prediction, including sequential labeling, parsing, and sentence generation. The prerequisites of this course are: Basic algebra, calculus, and probability theory; Introductory machine learning; Coding skills in any language (course projects will use python). |
| Science | CMPUT 652 | Machine Learning for Procedural Content Generation in Games | Winter 2021 | Matthew Guzdial | guzdial@ualberta.ca | MW 13:30-14:50 | The purpose of this course is for graduate students in Computing Science to gain a breadth of understanding of the machine learning approaches applied to procedural content generation and the problems specific to this area of research. This involves covering basic topics from other ML courses as they apply to content creation in games and more specialized topics related to generating content with AI. |
| Science | CMPUT 653 | Deep Policy Gradient Methods | Fall 2020 | Rupam Mahmood | ashique@ualberta.ca | MW 14:00-15:20 | When the input-output interface of a robot is determined, can we just deploy a general-purpose system for controlling the robot without extensive hand-engineering? Neural networks with parameters learned by policy gradient methods are a candidate for such systems that are already shown to learn controlling real robots from scratch. In this course, we learn the foundations of policy gradient methods and some of the fundamental differences between standard policy gradient methods such as actor-critic and those combining well with neural networks and achieving practical success such as Proximal Policy Optimization. We discuss a number of recent papers on policy gradient methods and conclude the course with a project guided by the instructor toward developing a mini-research contribution. Throughout the course, there will be a focus on computational frugality and compatibility with real-time updates. |
| Science | CMPUT 653 | Theoretical Foundations of Reinforcement Learning | Winter 2021 | Csaba Szepesvari | szepesva@ualberta.ca | TR 15:30-16:50 | The purpose of this course is to allow students to acquire a solid understanding of the theoretical foundations of reinforcement learning. The topics will range from building up foundations (Markovian Decision Processes and the various special cases of it), to discussing solutions to the three core problem settings: Online reinforcement learning, batch reinforcement learning and planning/simulation optimization. In each of these settings, we cover key algorithmic challenges and the core ideas to address these. Specific topics, ideas and algorithms covered include: the role of optimism in online RL for both reward maximization and learning with no rewards; UCRL2 and extensions to function approximation; key results in off-policy evaluation and optimization with and without function approximation; hardness of planning and various oracle models to overcome hardness; randomized planners. We explore some of the popular deep RL methods; their origin and what theoretical results are available for them. Students taking the course are expected to have an understanding of basic probability, basics of concentration inequalities, linear algebra and convex optimization. |
| Science | CMPUT 654 | Privacy in Machine Learning | Winter 2021 | Nidhi Hegde | nidhi.hegde@ualberta.ca | TR 14:00-15:20 | This course will focus on the design of machine learning algorithms for preserving privacy. We will study Differential Privacy, a mathematical framework for privacy, by focusing on recent work and applications. We will also study notions of fairness in machine learning. For both privacy and fairness we will study the theory and practice of designing private and fair machine learning algorithms with recent practical applications as a guide. |
| Science | CMPUT 654 | Reinforcement Learning I | Fall 2020 | Martha White | whitem@ualberta.ca | MW 11:00-12:20 | This course provides an introduction to reinforcement learning, which focuses on the study and design of agents that interact with a complex, uncertain world to achieve a goal. We will emphasize agents that can make near-optimal decisions in a timely manner with incomplete information and limited computational resources. The course will cover Markov decision processes, reinforcement learning, planning, and function approximation (online supervised learning). The course will use a recently created MOOC on Reinforcement Learning, created by the Instructors of this course. Much of the lecture material and assignments will come from the MOOC. In-class time will be largely spent on discussion and thinking about the material, with some supplementary lectures. The MOOC content will be covered more quickly (by mid October), so as to focus on research projects and reading RL papers in the second half of the course. |
| Science | CMPUT 656 | Interactive Machine Learning | Fall 2020 | Matthew E. Taylor | mtaylor3@ualberta.ca | TR 11:00-12:20 | How do you want to interact with AI systems? We don't know what tomorrow's AI systems will be capable of, but we do know that learning will be a critical component. Unlike traditional machine learning, interactive machine learning involves a learning AI and one or more humans. This can range from using humans to label data to human-in-the-loop to human-AI teaming. This course will provide you with the background and tools needed to conduct research in this emerging area. The primary goal of the class is to conduct a pilot study that, if successful, could lead to a conference paper at a venue like AAAI, NeurIPS, or CHI. To achieve this goal, students will learn basic skills required for human research, engage with interactive lectures, and read and discuss current research. This course will assume familiarity with machine learning --- students taking the class should have completed at least one prior machine learning course at the graduate or undergraduate level. Although the course will be recorded and made available to all interested students, students taking the course for credit will be expected to attend and participate in regular synchronous class meetings. |
| Science | CMPUT 658 | Single Agent Search | Winter 2021 | Nathan Sturtevant | nathanst@ualberta.ca | TR 14:00-15:20 | This course covers fundamental algorithms used for problem solving and search, including A*, IDA* and many others. A strong focus will be on heuristic estimates that guide search. We will look at where heuristics come from, what properties are necessary for their use, and how they can be generated. The course will look at both exponential and polynomial domains as well as the differences in their use. A heavy emphasis will be placed on programming assignments. |
| Science | CMPUT 659 | Program Synthesis in XAI | Winter 2021 | Levi Lelis | santanad@ualberta.ca | TR 17:00-18:25 | In this course we will study explainable and interpretable AI algorithms in the context of computer games. |
| Science | CMPUT 660 | Internet of Things Applications | Winter 2021 | Eleni Stroulia | stroulia@ualberta.ca | MW 10:30-11:50 | We are witnessing the emergence of a fascinating class of technologies, and systems built on them, generally referred to as "Internet of Things". The objective of this course is to introduce students to this broad area, including (but not limited to) in the context of creating a complete IoT application: working with sensors and actuators, and the communication protocols that can be used to connect them; programming on embedded and mobile platforms, such as Raspberry Pi; building RESTful web services on the cloud; analyzing and visualizing data. |
| Science | CMPUT 696 | Information Extraction and Knowledge Graphs | Winter 2021 | Denilson Barbosa | denilson@ualberta.ca | TR 14:00-15:20 | CMPUT 696 is a project-based course where students will be exposed to methods for the creation and curation of knowledge graphs and some of their applications. There are no formal prerequisites, but students are expected to have basic familiarity with natural language processing, query languages, machine learning and embeddings. |
| Science | GEOPH 426/526 | Signal Processing in Geophysics | Fall 2020 | Mauricio Sacchi | msacchi@ualberta.ca | TR 9:30-10:50 | Application of time series analyses and image processing techniques to large geophysical data sets; sampling of data and problems of aliasing; one and two dimensional Fourier transforms; the Z transformation; spectral analysis, filtering, and deconvolution; application of 1D and 2D filtering to seismic and gravity/magnetic data analysis. Prerequisites: MATH 311, GEOPH 326, PHYS 234 or equivalent. |
| Science | PSYCO 576 | Cognitive Neuroscience | Fall 2020 | Alona Fyshe | alona@ualberta.ca | MW 14:00-15:20 | This class will cover the basics of machine learning and general data analysis. The class has a large project component, and the hope is that students will come with their own datasets in hand and we will work on them collaboratively throughout the semester. Students may also use a publicly available dataset (e.g. from https://openneuro.org/). As part of the class we will workshop project ideas, discuss analyses, and provide feedback on each of the projects as they evolve. We will read related papers to help inspire our work. Part of the class will be devoted to producing a manuscript based on the student's chosen projects. Prerequisites: Students need some experience programming for this class. We will be using Python, but strong knowledge in another programming language could suffice. Knowledge of linear algebra and statistics will be helpful, but we will go over the basics in class. |
| Science | STAT 337 | Biostatistics | Fall 2020 | Keumhee Chough | kccarrie@ualberta.ca | MWF 13:00-13:50/TBD | Methods of data analysis useful in Biostatistics including analysis of variance and covariance and nested designs, multiple regression, logistic regression and log-linear models. The concepts will be motivated by problems in the life sciences. Applications to real data will be emphasized through the use of a computer package. Prerequisite: STAT 151 or SCI 151 and a 200-level Biological Science course. Note: This course may not be taken for credit if credit has already been obtained in STAT 252, 368 or 378. |
| Science | STAT 353 | Life Contingencies I | Winter 2021 | T Choulli | tchoulli@ualberta.ca | MWF 14:00-14:50/CAB 235 | Time at death random variables, continuous and discrete insurances, endowments and varying annuities, net premiums and reserves. Prerequisites: MATH 253 and STAT 265. Corequisite: MATH 215 or 317. |
| Science | STAT 361 | Sampling Techniques | Winter 2021 | R Karuamuni | rkarunam@ualberta.ca | MWF 13:00-13:50/CAB 273 | Simple random sampling from finite populations, stratified sampling, regression estimators, cluster sampling. Prerequisite: STAT 266, or STAT 235 with consent of the Department. Note: This course may only be offered in alternate years. |
| Science | STAT 368 | Introduction to Design and Analysis of Experiments | Fall 2020 | Keumhee Chough | kccarrie@ualberta.ca | TR 11:00-12:20/TBD | Basic principles of experimental design, completely randomized design-one way ANOVA and ANCOVA, randomized block design, Latin square design, Multiple comparisons. Nested designs. Factorial experiments. Prerequisite: STAT 266, or STAT 235 with consent of the Department. |
| Science | STAT 371 | Probability and Stochastic Processes | Winter 2021 | Michael Kouritzin | michaelk@ualberta.ca | TR 9:30-10:50/CAB 235 | Problem solving of classical probability questions, random walk, gambler's ruin, Markov chains, branching processes. Selected topics of the instructor's choice. Prerequisite: STAT 265. |
| Science | STAT 372 | Mathematical Statistics | Fall 2020 | R Karuamuni | rkarunam@ualberta.ca | MWF 13:00-13:50/TBD | Laws of large numbers, weak convergence, some asymptotic results, delta method, maximum likelihood estimation, testing, UMP tests, LR tests, nonparametric methods (sign test, rank test), robustness, statistics and their sensitivity properties, prior and posterior distributions, Bayesian inference, conjugate priors, Bayes estimators. Prerequisite: STAT 266. |
| Science | STAT 378 | Applied Regression Analysis | Fall 2020 | Bei Jiang | bei1@ualberta.ca | MWF 10:00-10:50/TBD | Simple linear regression analysis, inference on regression parameters, residual analysis, prediction intervals, weighted least squares. Multiple regression analysis, inference about regression parameters, multicollinearity and its effects, indicator variables, selection of independent variables. Non-linear regression. Prerequisite: STAT 266, or STAT 235 with consent of the Department. |
| Science | STAT 413/513 | Computational Statistics | Winter 2021 | Ivan Mizera | imizera@ualberta.ca | MWF 10:00-10:50/TBD | Introduction to contemporary computational culture: reproducible coding, literate programming. Monte Carlo methods: random number generation, variance reduction, numerical integration, statistical simulations. Optimization: linear search, gradient descent, Newton-Raphson, and their specifics in the statistical context like the method of scoring, EM algorithm. Fundamentals of convex optimization with constraints. Prerequisites: STAT 265 or equivalent and one of CMPUT 174 or 272. |
| Science | STAT 432/532 | Survival Analysis | Winter 2021 | Lingzhu Li | lingzhu@ualberta.ca | TR 12:30-13:50/ED 165 | Survival models, model estimation from complete and incomplete data samples, parametric survival models with concomitant variables, estimation of life tables from general population data. Prerequisites: STAT 372 and 378. |
| Science | STAT 437 | Statistical Methods for Applied Research I | Fall 2020 | Bei Jiang | bei1@ualberta.ca | MWF 9:00-9:50/TBD | |
| Science | STAT 441 | Statistical Methods for Learning and Data Mining | Winter 2021 | Ivan Mizera | imizera@ualberta.ca | MWF 9:00-9:50/TBD | Principles of statistical model building and analysis applied in linear and generalized linear models and illustrated through multivariate methods such as repeated measures, principal components, and supervised and unsupervised classification. Prerequisite: STAT 368 or 378. |
| Science | STAT 453/553 | Risk Theory | Fall 2020 | A Cadenillas | abel@ualberta.ca | TR 9:30-10:50/TBD | Classical ruin theory, individual risk models, collective risk models, models for loss severity: parametric models, tail behavior, models for loss frequency, mixed Poisson models; compound Poisson models, convolutions and recursive methods, probability and moment generating functions. Prerequisite: STAT 371. |
| Science | STAT 471 | Probability I | Fall 2020 | T Choulli | tchoulli@ualberta.ca | MWF 11:00-11:50/TBD | Probability spaces, algebra of events. Elements of combinatorial analysis. Conditional probability, stochastic independence. Special discrete and continuous distributions. Random variables, moments, transformations. Basic limit theorems. Prerequisite: STAT 371. |
| Science | STAT 479 | Time Series Analysis | Winter 2021 | Adam Kashlak | kashlak@ualberta.ca | TR 9:30-10:50/TBD | Stationary series, spectral analysis, models in time series: autoregressive, moving average, ARMA and ARIMA. Smoothing series, computational techniques and computer packages for time series. Prerequisites: STAT 372 and 378. Note: This course may only be offered in alternate years. |
| Science | STAT 512 | Techniques of Mathematics for Statistics | Fall 2020 | Linglong Kong | lkong@ualberta.ca | TR 9:30-10:50/TBD | Topics to be covered include a range of mathematical topics useful in Statistics - matrix manipulations (orthogonality, Gram-Schmidt decomposition, diagonalization), calculus and analysis (limits, continuity, dierentiation, sequences and series), multidimensional calculus and optimization (extrema, Lagrange multipliers, Steepest descent, Newton-Raphson, Gauss-Newton and Alternating Direction Method of Multipliers (ADMM)), all with an eye to applications in Statistics and Probability (linear and nonlinear least squares estimation,Laws of Large Numbers, Central Limit Theorem, generating functions, maximum likelihood estimation, etc.).Prerequisite: consent of Department. |
| Science | STAT 562 | Discrete Data Analysis | Fall 2020 | Linglong Kong | lkong@ualberta.ca | TR 12:30-13:50/TBD | This class provides an introduction to the theory and methods for the analysis of categorical response and count data, including description and inference for binomial and multinomial observations using proportions and odds ratios; multi-way contingency tables; generalized linear models for discrete data; logistic regression for binary responses; multi-category logit models for nominal and ordinal responses; inference for matched-pairs and correlated clustered data; loglinear models. Students will use R for statistical analyses. Prerequisite: STAT 372 or 471. |
| Science | STAT 566 | Methods of Statistical Inference | Fall 2020 | R Karuamuni | rkarunam@ualberta.ca | MWF 11:00-11:50/TBD | An introduction to the theory of statistical inference. Topics to include exponential families and general linear models, likelihood, sufficiency, ancillarity, interval and point estimation, asymptotic approximations. Optional topics as time allows, may include Bayesian methods, Robustness, resampling techniques. This course is intended primarily for MSc students. Prerequisite: STAT 471 or consent of Department. |
| Science | STAT 568 | Design and Analysis of Experiments | Winter 2021 | Adam Kashlak | kashlak@ualberta.ca | MWF 13:00-13:50/CAB 457 | The general linear model. Fully randomized designs, one-way layout, multiple comparisons. Block designs, Latin squares. Factorial designs confounding, fractions. Nested designs, randomization restrictions. Response surface methodology. Analysis of covariance. Prerequisite: STAT 368 and a 400-level STAT course. |
| Science | STAT 571 | Probability and Measure | Winter 2021 | TR 11:00-12:20/CAB 457 | Measure and integration, Laws of Large Numbers, convergence of probability measures. Conditional expectation as time permits. Prerequisites: STAT 471 and STAT 512 or their equivalents. | ||
| Science | STAT 575 | Multivariate Analysis | Winter 2021 | Ivan Mizera | imizera@ualberta.ca | MWF 14:00-14:50/CAB 457 | The multivariate normal distribution, multivariate regression and analysis of variance, classification, canonical correlation, principal components, factor analysis. Prerequisite: STAT 372 and STAT 512. |
| Science | STAT 578 | Regression Analysis | Fall 2020 | Lingzhu Li | lingzhu@ualberta.ca | MWF 14:00-14:50/TBD | Multiple linear regression, ordinary and generalized least squares, partial and multiple correlation. Regression diagnostics, collinearity, model building. Nonlinear regression. Selected topics: robust and nonparametric regression, measurement error models. Prerequisites: STAT 378 and a 400-level statistics course. |
| Science | STAT 580 | Stochastic Processes | Fall 2020 | TR 11:00-12:20/TBD | Elements of stochastic processes. Discrete and continuous time Markov Chains; Birth and Death processes. Branching processes. Brownian Motion. General Stationary and Markov processes. Examples. Prerequisite: STAT 471 or consent of Instructor. | ||
| Science | STAT 590 | Statistical Consulting | Winter 2021 | Bei Jiang | bei1@ualberta.ca | TR 14:00-15:20/CAB 377 | Data analysis, problem solving, oral communication with clients, issues in planning experiments and collecting data; practical aspects of consulting and report writing. Corequisite: STAT 568 and 578 or their equivalents. |
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