CS 274A: Probabilistic Learning: Theory and Algorithms

General Information

• Lectures on Monday and Wednesday, 5:00 to 6:20pm, MSTB 118.
• Instructor: Professor Padhraic Smyth, Office Hours: TBD
• Syllabus and Schedule
• Class Notes, Additional Reading, Textbooks:
  • Class notes and links to additional reading
  • Relevant textbooks for additional reading
• Questions? please use the Ed Discussion platform (accessible via Canvas) for all questions to the instructor and TA. You can either post your questions publicly to the class or privately to the instructor or TA (we recommend public posting in general for questions related to clarifications of lecture materials and homeworks so that all students can benefit from the responses). Please also feel free to answer questions that other students post. Please do not use email unless EdD does not work for some reason.

Course Goals

Students will develop a comprehensive understanding of probabilistic approaches to machine learning, a key component underlying areas such as artificial intelligence, natural language processing, speech recognition, computer vision, bioinformatics, and so forth. The course will provide a tutorial introduction to the basic principles of probabilistic modeling and then demonstrate the application of these principles to the analysis, development, and practical use of machine learning algorithms.

Topics covered will include probabilistic modeling, defining likelihoods, parameter estimation using likelihood and Bayesian techniques, probabilistic approaches to classification, clustering, and regression, and related topics such as model selection and bias/variance tradeoffs. Discussion of topics such as deep learning, generative models, and large language models will be incorporated where relevant.

Background Knowledge

Knowledge of basic concepts in probability, multivariate calculus, and linear algebra are important for this course. In particular a good understanding of basic concepts in probability (conditional probability, expectation, multivariate probability models, density functions, etc) is important. If you are not sure whether you have the relevant background or not, please take a look at Chapters 5.1 to 5.5 and 6.1 to 6.5 in Mathematics for Machine Learning: its fine if you have not seen all of this material before, but it will be helpful for this class if you are fairly comfortable with the level of notation and mathematics used in these chapters.

Homeworks

• There will be 5 or 6 homeworks during the quarter, including some small projects. Homeworks and deadlines will be posted here as they become available. Homeworks will be submitted and graded via Gradescope.

Grading Policy

Final grades will be based on a combination of homework assignments and exams: 40% homeworks, 30% midterm, and 30% final. Your lowest scoring homework will be dropped and not included in your score. No credit for late homeworks.

Academic Integrity

Students are expected to read and be familiar with the Academic Integrity Policy for this class. Failure to adhere to this policy can result in a student receiving a failing grade in the class.