Reuben-Cooke Building 127

Tuesday-Thursday: 8:30am-9:45am

**Office Hours:** Tuesday 10-11am, Thursdays 1-2pm. Gross Hall 3rd floor.

Recomended Prerequisites: Mathematics 230/340 and 218/216/221.

YOUTUBE PLAYLIST

**Intro to ML** (1 Lecture)

**Topics:** Regression & Classification, Supervised & Unsupervised Learning, Training, Bias-Variance Tradeoff, Model Complexity.

**Statistical Learning Theory** (2 Lectures)

**Topics:** Empirical Risk Minimization, Hypotheses Classes, PAC Learnability, No Free Lunch Theorem, VC Dimension.

**COURSE NOTES**

**Intro to NN **(3 Lectures)

**Topics:** Components of Neural Networks, Backprop, Cross-Entropy, Regularization, Dropout, Weight Initialization, Vanishing/Exploding Gradients, Universal Approximation Theorem.

**COURSE NOTES**

**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.

**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.

**Dimensionality Reduction **(3 lectures)

**Topics:** Curse and Blessing of Dimensionality, PCA, Kernel PCA, MDS, Random Projections, Johnson-Lindenstrauss, Isomap, Laplacian Eigenmaps.

**Bayesian Models & Gaussian Processes** (4 Lectures)

**Topics:** The multivariate Gaussian distribution, formulas for conditional and marginal Gaussians, the Gaussian distribution and Bayes’ Rule, the Bayesian Approach to Linear Regression, the predictive distribution, the equivalent kernel, … (**more to be added**)

**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.

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