Lecture 1: Introduction to ML, Linear Regression
- Chapter 1 and 2 from KM book.
Lecture 2: Linear Regression, Robust Regression, Overfitting, Regularization
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: Dimensionality Reduction via NNs and Autoencoders, Sparsity, Training Autoencoders
- See piazza for reading materials.
Lecture 15: Convolutional Neural Networks, Architectures, Training, Examples
- See piazza for reading materials.
Lecture 16: Recurrent Neural Networks, LSTMs, Architectures, Training, Examples, Visualization
- See piazza for reading materials.
Lecture 17: Centroid Clustering via KMeans, Subspace Clustering via KSubspaces
- See piazza for reading materials.
Lecture 18: Similarity Graphs, Graph Laplacian and its Properties, Spectral Clustering
Lecture 19: Spectral Clustering, Graph-Cuts
- Chapter 25 and 22 from KM book.
Lecture 20: Latent Variable Models, EM Algorithm, Mixture of Gaussians
Lecture 21: Sequential Data Modeling, Markov Models and Hidden Markov Models
- Chapter 10 and 17 from KM book.