Algorithms (Sorting, searching and graph algorithms)
Topics from probability theory and linear algebra as needed for machine learning.
Introduction to machine learning, supervised and unsupervised learning.
Finite state automata, decision trees and learning using finite state automata and decision trees.
Algorithms for large data, clustering theory, theory of high dimensional problems.
Optimization in machine learning.
Computational learning theory.
Machine learning on encrypted data and secure multi-party computation.
