Tentative Schedule of Topics

.

The following is a tentative schedule of what we will cover in class when. Check this space regularly for updates.

Last update: 12-03-21

Key to reading materials:

Date Topics Readings Assignments Due (11:59 PM)
M 10/04 What is (Probabilistic) Machine Learning? MLPP 1.1-1.3, BMRL 13.1
  [PART I: Machine Learning Fundamentals (~ 2 weeks)]  
W 10/06 Nearest Neighbor Classification FCML 5.3.1, BRML 14.1-14.2 Background Survey
  Evaluating a Classifier    
F 10/08 Cross-Validation FCML 1.5
M 10/11 Linear Regression: One Scalar Predictor FCML 1.1-1.4
W 10/13 Linear Regression: Vector Predictor   HW0: Coding Warmup
F 10/15 Linear Regression: Vector Predictor (cont’d)  
M 10/18 Linear Regression: Polynomials as Linear Models  
W 10/20 Linear Regression: Regularization   HW1: Classification
F 10/22 Linear Regression: Regularization (cont’d)    
  [PART II: Probability Fundamentals (~ 2 weeks)]  
M 10/25 Probability: Probability Spaces FCML 2.1
W 10/27 Probability: Random Variables FCML 2.2, BRML 1.1, 8.1-8.3 HW2: Regression
F 10/29 Probability: Discrete Random Variables FCML 2.2, BRML 1.1, 8.1-8.3
M 11/01 Probability: Joint and Conditional Probability FCML 2.3-2.6, BRML 1.1, 8.1-8.3
W 11/03 Probability: Continuous Random Variables FCML 2.3-2.6, BRML 1.1, 8.1-8.3
F 11/05 Probability: Continuous Random Variables
Sun 11/07     HW3: Probability Basics
M 11/08 Probablity: Expectation and Variance
  [PART III: Inference Fundamentals (~ 3 weeks)]    
W 11/10 Maximum Likelihood Estimation FCML 2.7-2.8, BRML 8.6, 8.8.1
F 11/12 Bias/Variance Decomposition FCML 2.9-2.11
Sun 11/14     HW4: Maximum Likelihood
M 11/15 Bias and Variance in Regression FCML 2.9-2.11
W 11/17 Bayesian Inference: Fundamentals FCML 3.1-3.3, BRML 1.2-1.4
F 11/19 Bayesian Inference: Conjugate Priors  
Sun 11/21     HW5: Bayesian Fundamentals
  THANKSGIVING BREAK    
M 11/29 Belief Networks and Conditional Independence FCML 3.6, BRML 3.1-3.3
W 12/01 Bayesian Inference: Model Averaging and Occam’s Razor  
  [PART IV: Bayesian Supervised Learning (~ 2 weeks)]  
F 12/03 Naive Bayes Classification: Formulation and Max Likelihood FCML 5.1-5.2.1.6, BRML 10.1-10.2
Sun 12/05     HW6: Bayesian Inference and Belief Networks
M 12/06 Bayesian Naive Bayes I: Posterior Inference FCML 5.2.1.7, BRML 10.3
W 12/08 Bayesian Naive Bayes II: Prediction and Classification Exam Distributed
F 12/10 Bayesian Regression I: Posterior Inference FCML 3.8, BRML 12.1-12.4
Sun 12/12     Exam Due
M 12/13 Bayesian Regression II: Prediction and Model Selection FCML 3.9, BRML 12.1-12.4
  [PART V: Unsupervised Learning and Latent Variable Models (~2 weeks)]  
W 12/15 Clustering I: K-Means FCML Ch. 6.1-6.2
F 12/17 Clustering II: Mixture Models FCML 6.3.1-6.3.2, BRML 20.1
M 12/20 Clustering III: The EM Algorithm FCML 6.3.3-6.3.8, BRML 20.2-20.3 HW7: Bayesian Classification and Regression
W 12/22 Video – Clustering IV: Gibbs Sampling FCML 6.3.9, 9.1-9.2, 10.1
  WINTER BREAK    
M 01/03 Review and Refresher  
W 01/05 Hidden Markov Models: Overview BRML 23.1-23.3, 23.5 Clear Project Topic With Me
F 01/07 HMMs: Prediction and Inference  
M 01/10 HMMs: Learning with EM  
W 01/12 HMMs: Bayesian Learning   HW9: Hidden Markov Models
F 01/14 Nonparametric Models  
Sun 01/23 FINAL PRESENTATIONS (2-4 PM)   Final Project Writeup