Indian Institute of Information Technology, Allahabad

Department of Information Technology

Course Syllabus  

1. Name of the Course: Probabilistic Machine Learning and Graphical Models

2. LTP structure of the course: 2-1-1

3. Objective of the course: Introduce probabilistic view on machine learning and discuss graphical models with Mathematical rigour and application in real problems. This course will make extensive use of probability, statistics, and optimization.

4. Outcome of the course: Student will understand about probabilistic machine learning and get exposer to current cutting edge research.After successfully attending the course, students have developed an in-depth understanding of probabilistic graphical models. They describe and analyze properties of graphical models, and formulate suitable models for concrete estimation and learning tasks. They understand inference algorithms, judge their suitability and apply them to graphical models in relevant applications.

5. Course Plan:

Component

Unit

Topics for Coverage  

Component 1

Unit 1

Probabilistic supervised learning.

Unit 2

Probabilistic Unsupervised learning

Component 2

Unit 3

Graphical Model representation, including Bayesian and Markov networks, and dynamic Bayesian networks. Probabilistic inference algorithms, both exact and approximate; Sampling; and learning methods for both the parameters and the structure of graphical models.

Unit 4

Encoder-Decoder, Variational Autoencoder, Generative Adversarial Network (GAN)

6. References:

  1. Kevin Murphy, “Machine learning: a probabilistic perspective”, MIT Press, 2012.
  2. Daphne Koller and Nir Friedman, Probabilistic Graphical Models: Principles and Techniques
  3. Michael I. Jordan, An Introduction to Probabilistic Graphical Models, in preparation. Course2: