This course covers the basic concepts and algorithms for artificial intelligence, data mining and machine learning. The main contents of the course are: 1. Artificial intelligence: a. Task environment b. Performance measure c. Problem solving by searching i. Uninformed search ii. Informed search iii. Constraint satisfaction problems 2. Data mining: a. Data and patterns b. Summary statistics and visualization c. Unsupervised feature selection i. Clustering d. Supervised feature selection i. Individual feature ranking ii. Feature subset selection 3. Machine learning: a. Cross validation b. Supervised learning i. K-nearest neighbors ii. Naïve Bayes iii. Decision trees iv. Support vector machines v. Neural networks