Artificial Intelligence

Content (Syllabus outline): Linear models. Linear regression. Linear discriminant analysis. Logistic regression. Gradient descent. Stochastic gradient descent. The machine learning approach. Cost functions. Empirical risk minimization. Maximum likelihood estimation. Model evaluation. Cross-validation. Feature selection. Search-based feature selection. Regularization. Tree-based models. Decision trees. Random forest. Bagging. Gradient tree boosting. Clustering. k-means. Expectation Maximization. Non-linear regression. Basis functions. Splines. Support vector machines. Kernel trick. Neural networks. Perceptron. Activation functions. Backpropagation.