Lectures
#  DATE  TOPIC  NOTES  

1  Aug 26  Introduction  Overview of topics and applications  (none)  
2  Aug 31  Supervised Learning  Linear Regression: gradient descent, Normal equations.  
3  Sep 2  Supervised Learning  Probabilistic Interpretation, Logistic Regression  
4  Sep 7  Supervised Learning  Newton's method, Locally weighted Linear Regression, Nearest Neighbors  (previous pdf)  
5  Sep 9  Supervised Learning  Exponential Families, Generalized Linear Models  (previous pdf), Optional: Paper 

6  Sep 14  Optimization  Convex functions, Convex problems  pdf (pages 124), pdf (pages 123) 

7  Sep 16  Supervised Learning  Generative Learning Algorithms, Gaussian Discriminant Analysis  
8  Sep 21  Supervised Learning  Generative (contd.), Model and feature selection  Feature selection  
9  Sep 23  Supervised Learning  Kernels  Parts of Bishop, ch 6. Parts of Notes 

10  Sep 28  Supervised Learning  SVM.  Duality (pages 16, 813)  
11  Sep 30  Unsupervised Learning  Curse of Dimensionality, Dimensionality Reduction, PCA  Bishop, ch 12. (or PCA notes)  
12  Oct 14  Adaboost  Object Detection  PPTX  
13  Oct 19  Unsupervised Learning  Mixture of Gaussians, EM  
14  Oct 21  Unsupervised Learning  examples of EM, clustering, spectral clustering  PDF,
kmeans notes. Spectral. 

  Oct 22, 24pm  Midterm Project presentation  Details  
15  Oct 26  Unsupervised Learning  Multidimensional Scaling (MDS), Isomaps  Isomap paper  
15  Oct 28  Review  Independent Component Analysis (ICA), Learning Review  
  Oct 28  Midterm Exam, 7:3010pm  Supervised+Unsupervised Learning+Optimization+Theory  THR 203  
18  Nov 2  Unsupervised Learning  Non Negative Matrix Factorization  Bishop, ch 12. (Partial/complementary material covered here.) Full details in Prob PCA paper. NNMF Paper 

17  Nov 4  
19  Nov 9  Probabilistic Graphical Models  Introduction, Representation, Markov Blanket, variable elimination 
Bishop, ch 8. Others (not necessarily relating directly to the lecture notes): html, pdf 

20  Nov 11  Probabilistic Graphical Models  HMM, Inference on a chain (sumproduct specific case)  Bishop, ch 8.  
21  Nov 16  Probabilistic Graphical Models  Kalman Filters  Bishop, ch 8. slides  
22  Nov 18  Probabilistic Graphical Models  Directed / Undirected graphs, MRFs  Bishop, ch 8.  
23  Nov 23  Probabilistic Graphical Models  Sumproduct, Maxproduct  Bishop, ch 8.  
24  Nov 23  Probabilistic Graphical Models  Examples. MRF: discrete (imagedenoising), continuous (depth estimation), sampling/particle filters  Optional reading: Paper, Paper  
25  Nov 30  Paper reading  Graphical Models  Paper 1, Paper 2  
25  Dec 2  Special Topic  Deep Learning  Paper 1, Paper 2  
  Dec 110  Peer Review Period  Review 2 other reports  
  Dec 17, Friday  25pm  Projects 