Machine learning aims to develop computer programs that improve their performance through experience by capturing relevant abstractions of past training input. This course will cover topics in machine learning such as concept learning with version spaces, learning decision trees, statistical learning methods, genetic algorithms Bayesian learning methods, explanation-based learning, and reinforcement learning. Theoretical aspects such as inductive bias, the probably approximately correct learning, and minimum description length principle will also be covered.