Components of learning –learning models –geometric models –probabilistic models –logic models –grouping and grading –learning versus design –types of learning –supervised –unsupervised –reinforcement –theory of learning –feasibility of learning–error and noise –training versus testing –theory of generalization –generalization bound –approximation-generalization tradeoff –bias and variance –learning curve

Linear classification –univariate linear regression –multivariate linear regression –regularized regression –Logistic regression –perceptrons –multilayer neural networks –learning neural networks structures –support vector machines –soft margin SVM –going beyond linearity –generalization and overfitting –regularization –validation

Nearest neighbormodels –K-means –clustering around medoids –silhouttes –hierarchical clustering –k-d trees –locality sensitive hashing–non-parametric regression –ensemble learning –bagging and random forests –boosting –meta learning

Decision trees –learning decision trees –ranking and probability estimation trees –regression trees –clustering trees –learning ordered rule lists –learning unordered rule lists –descriptive rule learning –association rule mining –first-order rule learning

Introduction to scikit-learn library – supervised learning – k nearest neighbors – linear regressions – support vector machines – support vector regression