CS 274A: Probabilistic Learning: Theory and Algorithms, Winter 2018
General Information
 Time: Monday and Wednesday, 11:00 to 12:20pm
 Location: ICS 180
 Instructor:
Professor Padhraic Smyth: Office Hours, 9:30 to 11:30, Fridays, DBH 4216
 Reader: Vaibhav Pandey, email vaibhap1 at uci.edu. Office Hours: Tuesdays, 3 to 5pm, DBH 3209

Syllabus
 Notes: Links to notes, texts, and background
reading
 Questions? please use the
Piazza class Website for classrelated questions and discussion. If
you have a question please post it to Piazza (either publicly to the class, or privately to the instructor) rather than by sending an email.
Homeworks
 Homework 1
 PDF  LaTeX
Hardcopy due at the start of class on Wednesday January 17th.
 Homework 2
 PDF  LaTeX
Hardcopy due to Vaibhav's office DBH 3209 by Friday January 26th, drop off between 3pm and 5pm
 Homework 3
 PDF  LaTeX
Hardcopy due at the start of class on Monday February 5th.
 Homework 4
 PDF  Electronic copy to EEE, due by 11:55pm Friday Feb 23rd
 Homework 5
 PDF  LaTeX
Hardcopy due in class on Wednesday March 7th.
 Homework 6  PDF  data and sample code
Code and report to be uploaded to EEE by 11:45pm Friday March 16th.
Prerequisites for taking this class
Knowledge of basic concepts in probability, multivariate calculus, and linear algebra are required for this course.
Please note that a good understanding of probability in particular 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.
No credit for late homeworks  instead your lowest scoring homework will be dropped and not included in your score.
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.