CS 274A: Probabilistic Learning: Theory and Algorithms, Winter 2018
- Time: Monday and Wednesday, 11:00 to 12:20pm
- Location: ICS 180
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
- Notes: Links to notes, texts, and background
- Questions? please use the
Piazza class Website for class-related 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.
- 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
- Homework 4
- Homework 5
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.
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.
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.
Students are expected to be read and be familiar with the Academic
for this class.
Failure to adhere to this policy can result in a student receiving a failing grade in the class.