Prerequisites: familiarity with algorithm runtime analysis (e.g., big O notations), probability theory (e.g. Gaussian distribution and conditional probability) and programming language (e.g., MATLAB or C++). The course covers basic concepts and algorithms for artificial intelligence, data mining and machine learning. The main contents are: artificial intelligence (task environment, performance measure and problem solving by searching), data mining (data and patterns, summary statistics and visualization, unsupervised feature selection and supervised feature selection) and machine learning (cross validation and supervised learning).