Course Syllabus

Review of machine learning Concepts, Design of ML system – Model selection, bias, variance, learning curves, and error analysis

Recommendation Systems – Model for Recommendation Systems, Utility Matrix, Content- Based Recommendations, Discovering Features of Documents, Collaborative Filtering.

Mining Social network graphs – Clustering of Social Network Graphs, Partitioning of Graphs, and Finding Overlapping Communities

Advertising on the Web: Issues in Online Advertising, Online and offline algorithms, The matching Problem, The AdWords Problem, The Balance Algorithm, A Lower Bound on Competitive Ratio for Balance.

Application of dimensionality reduction in Image Processing – compression and Visualization.

Sparse models, State space models, Markov random Fields, Review of Inference for graphical models, Latent Linear and Variable models for discrete data, random algorithms in Computational Linear algebra.

Course Outcome

Evaluation Pattern:

• Periodical 1 – 10
• Periodical 2 – 10
• Lab – 20
• Project – 30
• End Semester – 30

At the end of the course the students will be able to;

1. Anand Rajaraman, Jure Leskovec and J.D. Ullman, “Mining of Massive Data sets”, e-book, Publisher, 2014.
2. Kevin P. Murphey, “Machine Learning, a Probabilistic Perspective”, The MIT Press Cambridge, Massachusetts, 2012,
3. Selected papers.