Topic

No. of hours

Lecture

Tutorial/Practical

Probability and Learning from a Bayesian Perspective 1, Parameter Estimation from Data, Mixture Models and the Expectation Maximization Algorithm.

6

6

-

Graphical Models, Classification, Clustering

 Power-Law Distributions.

3

3

-

Web Documents, Resource Identifiers: URI, URL, and URN.

3

3

-

Protocols, Log Files, Search Engines.

3

3

-

Internet and Web Graphs, Generative Models for the Web Graph and Other Networks.

3

3

-

Text Analysis, Indexing.

Lexical Processing, Content-Based Ranking, Probabilistic Retrieval, Latest Semantic Analysis.

6

6

-

Link Analysis,  Early Approaches to Link Analysis, Nonnegative Matrices and Dominant Eigenvectors, Hubs and Authorities: HITS  Page Rank Stability.

3

3

-

Advanced Crawling Techniques,

Selective, focused, and distributed

Crawling , Web Dynamics.

3

3

-

Modeling and Understanding Human Behavior on the Web.

6

6

-

Commerce on the Web: Models and Applications.

3

3

-

Registers.

3

3

-