Program overview Schedule Intro to ML, Linear Regression Linear Regression, Overfitting, ML Pipeline, Classification, k-NN, More on Regression,Gradient Descent Gradient Descent, Regularization, Decision theory, Empirical Risk Minimization Bias-Variance Tradeoff, linear classification GLM, GDA, Naive Bayes Logistic Regression, Newton-Raphson method, Evaluation Metrics, Perceptron Max-margin Classifiers, SVMs Decision trees, Ensembles: Bagging, Random Forests, Stackin Boosting, Neural Nets, Backpropagation Backpropagation, Deep Neural Networks, Convolutional Networks Optimization, Dimensionality Reduction, PCA, LDA Bayesian learning, MLE, MAP, Bayesian Linear Regressio Kernel Methods, Gaussian Process, Frontiers, What Next?