amlp.html

Advanced Machine Learning (CS60073)

Instructor: Pabitra Mitra, CSE, <pabitra@cse.iitkgp.ac.in> Phone: 03222-282356

Course Outline:
The course aims at covering topics related to probabilistic machine learning. The goal is to quantify the uncertainty involved in prediction and inference. Tools and techniques related to probabilistic Bayesian inference will be covered. Both exact and approximate inference techniques are considered.

Books:

Kevin P. Murphy Machine Learning: a Probabilistic Perspective, the MIT Press (2012).

David Barber Bayesian Reasoning and Machine Learning, Cambridge University Press (2012), (free online)

Christopher M. Bishop Pattern Recognition and Machine Learning. Springer (2006)

David J.C. MacKay Information Theory, Inference, and Learning Algorithms, Cambridge University Press (2003), available freely on the web.

Lecture Schedule:

Topic and Slides	Lecture Video	Exercise Problems
Basics of Probability
Probabilistic Models and Bayesian Inference	Lecture-1 Lecture 2 Lecture 3
Conditional Models	Lecture 4 Lecture 5	Problem Set-1
Gaussian Processes	Lecture 6 Lecture 7 Lecture 8
Latent Variable Models	Lecture 9 Lecture 10 Lecture 11
Variational Inference	Lecture 12 Lecture 13 Lecture 14
Markov Chain Monte Carlo Inference	Lecture 15 Lecture 16 Lecture 17	Problem Set-2
Topic Models	Lecture 18 Lecture 19 Lecture 20 Lecture 21 Lecture 22
Probabilistic Graphical Models	Lecture 23 Lecture 24 Lecture 25 Lecture 26 Lecture 27 Lecture 28 Lecture 29	Problem Set-3
Bayesian Deep Learning	Lecture 30

Past Exam Questions: Class Test 1 2020 Class Test 2 2020 Class Test 3 2020 Quiz 1 2021 Class Test 1 2021 Class Test 2 2021 Class Test 1 2020

Programming Assignments: Assignment 1 2020 Dataset Assignment 2 2020 Assignment 1 2021 Dataset Assignment 2 2021

Acknowledgements:
Course on Topics in Probabilistic Modeling and Inference by Prof. Piyush Rai at IIT Kanpur
Course on Probabilistic Machine Learning by Prof. Dr. Philipp Hennig at University of Tubingen
Course on Advanced Probabilistic Machine Learning at University of Uppsala