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 classrelated 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.