Lecture 3: Point Estimation, Maximum Likelihood Estimation, MAP Estimation

Lecture 4: Bayesian Learning, Generative Modeling for Classification

Lecture 5: Generative Modeling for Classification: Naive Bayes, Gaussian Discriminant Analysis

- Chapters 3 and 4 from KM book.

Lecture 6: Discriminative Modeling for Classification: Logistic Regression, Softmax Regression

Lecture 7: Perceptron Algorithm, Functional and Geometric Margins, Support Vector Machines (SVM)

- Chapters 8 and 14 from KM book.

Lecture 8: Support Vector Machines (SVM), Max-Margin Classification, Lagrange Duality, KKT Conditions

Lecture 9: Kernels, Kernel SVM, Soft-Margin SVM, SMO Algorithm, Multi-Class SVM

Lecture 10: Ensemble Methods, Bagging, Boosting

Lecture 11: Neural Networks, Architectures, Activations, Outputs, Forward Propagation

- See piazza for reading materials.

Lecture 12: Feed Forward NNs, Forward and Backward Propagation, Training via Backpropagation Algorithm

- See piazza for reading materials.

Lecture 13: Dimensionality Reduction, PCA, Kernel PCA

- Chapter 12 and 14 from KM book.

Lecture 14: Nonlinear Dimensionality Reduction, Locally Linear Embedding

- See piazza for reading materials.

Lecture 15: Dimensionality Reduction via NNs and Autoencoders, Sparsity, Training Autoencoders

- See piazza for reading materials.

Lecture 16: Convolutional Neural Networks, Architectures, Training, Examples

- See piazza for reading materials.

Lecture 17: Convolutional Neural Networks, Architectures, Training, Examples

- See piazza for reading materials.

Lecture 18: Centroid Clustering via KMeans, Subspace Clustering via KSubspaces

- See piazza for reading materials.

Lecture 19: Similarity Graphs, Graph Laplacian and its Properties, Spectral Clustering

Lecture 20: Spectral Clustering, Graph-Cuts

- Chapter 25 and 22 from KM book.

Lecture 21: Latent Variable Models, EM Algorithm, Mixture of Gaussians