ML4001 Probabilistic Graphical Models Syllabus:
ML4001 Probabilistic Graphical Models Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
To understand basic concepts of probabilistic graphical models
To explore different aspects of representation of probabilistic graphical models
To study different inference techniques
To apply various inference techniques
To understand learning associated with probabilistic graphical models
UNIT I INTRODUCTION
Probabilistic Graphical Models – Motivation –Foundations – Probability Theory –Graphs – Independence Properties – Bayesian Network Representation – Independence in Graphs – From Distribution to Graphs
UNIT II REPRESENTATION
Undirected Graphical Models – Parameterization –Markov Network Independencies – Bayesian Networks and Markov Networks – Local Probabilistic Models – Tabular CPDs – Template –Based Representation – Temporal Models- Exponential Family – Entropy and Relative Entropy
UNIT III INFERENCE
Exact Inference – Variable Elimination- Conditioning – Clique Trees – Message Passing – Inference as Optimization – Exact Inference as Optimization – Propagation based Approximation
UNIT IV ADVANCED INFERENCE
Particle Based Approximate Inference – Forward Sampling – Markov Chain Monte Carlo Methods – Map Inference – Variable Elimination for Map – Max-Product in Clique Trees – Exact Inference in Temporal Models
UNIT V LEARNING
Learning Graphical Models – Overview – Goals – Learning Tasks –Maximum Likelihood Estimation for Bayesian Networks – Bayesian Parameter Estimation – Structure Learning in Bayesian Networks -Methods –Learning Undirected Models
SUGGESTED ACTIVITIES:
1. Problems in Probability
2. Design examples of Probabilistic Graphical Models
3. Hand simulate all inferences possible with graphical models for examples of your choice
4. Give an example for temporal probabilistic graphical model
5. Discuss pros and cons of different learning techniques
COURSE OUTCOMES:
CO1: Understand basic concepts of probabilistic graphical models
CO2: Automatically convert a problem into a probabilistic graphical model
CO3: Implement a simple graphical model
CO4: Understand issues associated with temporal models
CO5: Design a learning system for the graphical model
TOTAL:45 PERIODS
REFERENCES
1. D. Koller and N. Friedman, “Probabilistic Graphical Models: Principles and Techniques”, MIT Press, 2009.
2. Probabilistic Machine Learning: An Introduction by Kevin Patrick Murphy.MIT Press, March 2022.
3. M.I. Jordan, “An Introduction to Probabilistic Graphical Models”, Preprint.
4. C.M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2006.
5. K.P. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2012.
6. David Barber. “Bayesian Reasoning and Machine Learning”, Cambridge University Press. 2012.
7. David Mackay, “Information Theory, Inference, and Learning Algorithms”, Cambridge university press. 15 February 2010.