Advanced topics in Machine Learning

Introduction: Overview of machine learning, Machine learning applications and examples; Reinforcement learning: Elements of reinforcement learning, Model based learning, Temporal difference learning, Generalization; Genetic Algorithms: Genetic operators, fitness function, Hypothesis space search, Genetic programming; Support Vector Machines: Optimal separating hyperplane, softmargin hyperplane, kernel functions, SVMs for regression; Combining learners: Voting, Bagging, Boosting; Assessing and Comparing Classification Algorithms: Cross-validation and resampling, Measuring error, Assessing performance, Comparing multiple classification algorithms.