CS 274A: Probabilistic Learning: Theory and Algorithms, Winter 2021

General Information

• Time: Monday and Wednesday, 9:30 to 10:50am
• Location: lectures on Zoom at scheduled times. Recordings of lectures should be available afterwards via Canvas
• Instructor: Professor Padhraic Smyth: Office Hours, Wednesdays 5:15pm to 6:30pm
• TA Hao Tang (50% time): Office Hours, Mondays 5 to 6pm
• Syllabus and Schedule
• Notes, Texts, and Additional Reading: Links to notes, texts, and background reading
• Questions? please use the Piazza class Website for class-related questions and discussion, i.e., post to Piazza (either publicly to the class, or privately to the instructor or reader) and do not use email.

Homeworks

• Approximately 5 or 6 homeworks during the quarter
• Homeworks will be submitted and graded via Gradescope.
• Homework 1 | PDF | LaTeX | Due by 1pm Tuesday January 12th.
• Homework 2 | PDF | LaTeX | Due by 1pm Friday Jan 22nd.
• Homework 3 | PDF | LaTeX | Due by 11:59pm Monday February 1st.
• Homework 4 | PDF | LaTeX | data | Due by noon Sunday February 21st (extended from original deadline of 6pm Friday February 19th).
• Homework 5 | PDF | LaTeX | Due by 11:59pm Monday March 1st.
• Homework 6 | PDF | LaTeX | Data and Sample Code | Due by 11:59pm Friday March 12th. Submit PDF report to Gradescope. Submit code to Canvas as a Zip file.

Prerequisites for taking this class

Knowledge of basic concepts in probability, multivariate calculus, and linear algebra are required for this course. Note in particular that a good understanding of basic concepts in probability is important for this class.

Course Goals

Students will develop a comprehensive understanding of probabilistic approaches to machine learning. Probabilistic learning is a key component in many areas within modern computer science, including artificial intelligence, data mining, 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.

Grading Policy

Final grades will be based on a combination of homework assignments and exams: 30% homeworks, 30% midterm, and 40% 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 be 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.