THIS SYLLABUS IS STILL A DRAFT AND SUBJECT TO REVISION

**Intro to ML** (1 Lecture)**Topics:** Regression & Classification, Supervised & Unsupervised Learning, Training, Test, and Validation Data, Bias-Variance Tradeoff, Model Complexity, Sensitivity, Specificity, TF, FP.

**Statistical Learning Theory** (2 Lectures)**Topics:** Empirical Risk Minimization, Hypothesis Classes, PAC Learnability, No Free Lunch Theorem, Ugly Duckling Theorem, VC Dimension.

**Tree Models** (2 Lectures)**Topics:** Decision, Regression, and Classification Trees, Purity Measures, Feature Importance, CART, Pruning, Boostrap, Bagging, Random Forest, Boosting, AdaBoost.

**Dimensionality Reduction** (2 Lectures)**Topics:** Curse and Blessing of Dimensionality, PCA, Kernel PCA, LLE, MDS, Random Projections, Johnson-Lindenstrauss, Isomap, Laplacian Eigenmaps.

**Optimization Theory** (4 Lectures)**Topics:** Convex Geometry, Convex Functions, 1st and 2nd Order Conditions for Convexity, Convex Optimization, Logistic Regression, SVM, Linear Search, Gradient Descent, Newton’s Method, Subgradients, SGD, Momentum.

**Intro to NN **(2 Lectures)**Topics:** Components of Neural Networks, Backprop, Cross-Entropy, Regularization, Dropout, Weight Initilization, Vanishing/Exploding Gradients, Universal Approximation Theorem.

**Convolutional Neural Networks** (2 Lectures)**Topics:** Filters, Pooling, Convolution, Stride, Boundary, Translation Equivariance, Reduction of Complexity, CNN + GANs, CNN on Graphs.

**Representation Learning** (2 Lectures)**Topics:** Window Co-Occurence, CBOW, Skip-Gram, RNNs, Information Theory, Hierarchical Softmax, Sentiment Analysis, Various Autoencoder Architectures.

**Reinforcement Learning** (4 Lectures)**Topics:** k-Bandit Problem, Greed vs. Exploration, Environments, States, Actions, Rewads, Agents, Policies, Value Functions, Bellman Equations, Policy Evaluation, Policy Iteration, Optimality Equations, Convergence Theorems, Monte-Carlo Methods, Temporal-Difference Methods.