STAT 940: Deep Learning (Fall 2023)
- Lec 1: Deep Learning, Motivation and course adminisrations, Lecture 1 slides (PDF)
- Lec 2: Feedforward Neural Network, Backpropagation, Lecture 2 slides (PDF)
- Lec 3: Optimization, Lecture 3 slides (PDF)
- Lec 4: Regularization, Lecture 4 slides (PDF)
- Lec 5: Dropout, Batch Normalization, Lecture 5 slides (PDF)
- Lec 6: Convolutional Neural Networks (CNN), Lecture 6 slides (PDF)
- Lec 7: Regularization (Layer norm, FRN, TRU), Keras, Lecture 7 slides (PDF)
- Lec 8: Recurrent neural network (RNN), Lecture 8 slides (PDF)
- Lec 9: Attention mechanism, self-attention, S2S, Lecture 9 slides (PDF)
- Lec 10: Transformers, Lecture 10 slides (PDF)
- Lec 11: BERT and GPT, Lecture 11 slides (PDF)
- Lec 12: Deep Reinforcement Learning (Part 1), Lecture 12 slides (PDF)
- Lec 13: Deep Reinforcement Learning (Part 2), Lecture 13 slides (PDF)
- Lec 14: RLHF, ChatGPT, Alignment in LLMs, Lecture 14 slides (PDF)
- Lec 15: Variational Autoencoder, VAE, Performer, Lecture 15 slides (PDF)
- Lec 16: Generative Adversarial Networks (GAN), AAE, Lecture 16 slides (PDF)
- Lec 17: Diffusion Models, DDPMs, Lecture 17 slides (PDF)
- Lec 18: Graph Neural Network (Part 1), Lecture 18 slides (PDF)
- Lec 19: Graph Neural Network (Part 2), Lecture 19 slides (PDF)
- Lec 20: PAC Learnability in Deep Learning, Lecture 20 slides (PDF)
STAT 442/842: Data Visualization, a course on unsupervised learning (2017)
- Lec 1: Principal Component Analysis
- Lec 2: PCA (Ordinary, Dual, Kernel)
- Lec 3: FDA
- Lec 4: MDS, Isomap, LLE
- Lec 5: LLE, Spectral Clustering
- Lec 6: Spectral Clustering, Laplacian Eigenmap, MVU
- Lec 7: MVU, Action Respecting Embedding, Supervised PCA
- Lec 8: Supervised PCA
- Lec 9: SPCA, Nystrom Approximation, NMF
- Lec 10: NMF via R1D algorithm
- Lec 11: Sum-Product Networks
- Lec 12: Neural Networks, Autoencoders, Word2Vec
- Lec 13: Word2Vec Skip-Gram
- Lec 14: Autoencoders, Clustering, Mixture of Gaussians
- Lec 15: t-SNE
- Lec 16: Variational Autoencoders
STAT 441/841: Statistical Learning — Classification (Winter 2017), playlist
- Lec 1: Intro to classifiers, Bayesian classifiers, LDA and QDA, slides
- Lec 2: QDA, PCA
- Lec 3: FDA
- Lec 4: Logistic regression
- Lec 5: Model selection, Neural Networks
- Lec 6: Spectral Clustering, Laplacian Eigenmap, MVU
- Lec 7: Back Propagation, RBN
- Lec 8: Complexity control for RBN
- Lec 9: Regularization, Hard Margin SVM
- Lec 10: SVM, Kernel SVM
- Lec 11: Soft Margin SVM
- Lec 12: Metric Learning
- Lec 13: SPCA, Naive Bayes, K-nearest neighbour
- Lec 14: Convolutional Neural Networks
- Lec 15: Random features, Tree
- Lec 16: Tree, Boosting method
- Lec 17: Boosting method
- Lec 18: Bagging
Deep Learning (2017) all videos and slides
- Sep 7: Introduction (no video), slides
- Sep 12: Perceptron, FFNN, Backpropagation, slides
- Sep 14: Overfitting, Regulatization
- Sep 19: Weight Decay, Introduction to Keras, slides
- Sep 26: Regularization, Dropout, slides
- Sep 28: Batch Normalization, CNN, slides
- Oct 3: CNN, slides
- Oct 5: RNN, slides
- Oct 12 Part 1: Variational Autoencoder
- Oct 12 Part 2: Variational Autoencoder
- Oct 17: Sum Product Network, slides
- Oct 19: Deep Reinforcement Learning, slides
- Oct 24: Generative Adversarial Networks, slides
STAT 441/841, CM 763: Statistical Learning Classification (Fall 2015) all videos and slides
- Lec 1: Machine Learning, Introduction
- Lec 2: Formal definition of classification, Linear discriminant analysis (LDA), Quadratic discriminant analysis (QDA)
- Lec 3: QDA, Principal Component Analysis (PCA)
- Lec 4: PCA,Fisher’s Discriminant Analysis (FDA)
- Lec 5: Logistic Regression
- Lec 6: Logistic Regression, Perceptron
- Lec 7: Backpropagation
- Lec 8: Radial Basis Function Networks
- Lec 9: Stein’s unbiased risk estimate (sure)
- Lec 10: Weight decay
- Lec 11: Hard margin svm
- Lec 12: Soft margin svm
- Lec 13: Dual PCA, Supervised PCA
- Lec 14: Supervised PCA, Decision tree
- Lec 15: Decision Tree, KNN
- Lec 16: Boosting
- Lec 17: Bagging, Convolutional Networks (part 1)
- Lec 18: Convolutional neural network (part 2)
- Lec 19: PAC Learning
STAT 946 Topics in Probability and Statistics: Deep Learning (Fall 2015) all videos and slides
- Course Outline, list of papers
- Lec 1.1: Introduction, slides
- Lec 1.2: Perceptron, Feedforward Neural Network, Back propagation
- Lec 2.1: Regularization, slides
- Lec 2.2: Regularization
- Lec 3.1: Word2vec, slides
- Lec 3.2: Word2vec
- Lec 4.1: Sum-Product Networks, slides
- Lec 4.2: Sum-Product Networks
- Lec 5.1: Recurrent neural network, slides
- Lec 5.2: Recurrent neural network
- Lec 6: Convolutional network, slides
- Lec 7: Restricted Boltzmann Machine (RBM), slides
- Theano Tutorial, example.ipyng, lasagne_example.ipynb, lstm.ipynb
- Keras Tutorial, slides