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This online course (MOOC) explores the basics of Machine Learning.The Stanford course provides a broad introduction to machine learning, datamining, and statistical pattern...

This series of MOOCs explores the fundamentals of probabilistic graphical models.

This course is a continuation of Crypto I and explains the inner workings of public-key systems and cryptographic protocols. Students will learn how to reason about the security...

This course teaches how to model social and economic networks and their impact on human behavior. How do networks form, why do they exhibit certain patterns, and how does their...

This online course discusses the key problems of parameter estimation in both directed and undirected models, as well as the structure learning task for directed models.

Cryptography is an indispensable tool for protecting information in computer systems. This course explains the inner workings of cryptographic primitives and how to correctly...

This online course explores data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search,...

This online course explores greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal...

In this class, students will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques.

This online course presents both exact and approximate algorithms for different types of inference tasks, and discusses where each could best be applied.

This series of MOOCs gives an introduction to algorithms.

This online course explores topics such as asymptotic notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and...