STAT 339 (Spring 2020): 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: 08-29-21

Key to reading materials:

Date Topics Readings Assignments
M 2/03 What is (Probabilistic) Machine Learning? MLPP 1.1-1.3, BMRL 13.1
  [PART I: Machine Learning Fundamentals (~ 2 weeks)]  
W 2/05 Nearest Neighbor Classification FCML 5.3.1, BRML 14.1-14.2 Background Survey Due
  Evaluating a Classifier    
F 2/07 Cross-Validation FCML 1.5
M 2/10 Linear Regression: One Scalar Predictor FCML 1.1-1.4
W 2/12 Linear Regression: Vector Predictor   HW0: Optional Warmup due
F 2/14 Linear Regression: Vector Predictor (cont’d)  
M 2/17 Linear Regression: Polynomials as Linear Models  
W 2/19 Linear Regression: Regularization HW1a: Classification due
  [PART II: Probability Fundamentals (~ 2 weeks)]  
F 2/21 Probability: Probability Spaces FCML 2.1
M 2/24 Probability: Discrete Random Variables FCML 2.2, BRML 1.1, 8.1-8.3
W 2/26 Probability: Discrete Random Variables (cont’d) FCML 2.2, BRML 1.1, 8.1-8.3 HW1b: Regression due
F 2/28 Probability: Joint and Conditional Probability FCML 2.3-2.6, BRML 1.1, 8.1-8.3
M 3/02 Probability: Continuous Random Variables FCML 2.3-2.6, BRML 1.1, 8.1-8.3
W 3/04 Probability: Expectation and Variance FCML 2.3-2.6, BRML 1.1, 8.1-8.3 [HW2a: Probability Basics](../hw/hw2a-probability.pdf) due
  [PART III: Inference Fundamentals (~ 3 weeks)]
F 3/06 Maximum Likelihood Estimation FCML 2.7-2.8, BRML 8.6, 8.8.1
M 3/09 Bias/Variance Decomposition FCML 2.9-2.11
W 3/11 Bias and Variance in Regression FCML 2.9-2.11 HW2a: Probability Basics due
      HW2b: Maximum Likelihood Estimation due
F 3/13 (Zoom / Logistics Discussion)
3/16-3/27 EXTENDED SPRING BREAK    
M 3/30 Bayesian Inference: Fundamentals FCML 3.1-3.3, BRML 1.2-1.4
W 4/01 Bayesian Inference: Conjugate Priors  
F 4/03 Bayesian Inference: Exponential Families  
M 4/06 Belief Networks and Conditional Independence FCML 3.6, BRML 3.1-3.3
W 4/08 Bayesian Inference: Model Averaging and Occam’s Razor   HW3: Bayesian Inference and Belief Networks due
  [PART IV: Bayesian Supervised Learning (~ 2 weeks)]  
F 4/10 Naive Bayes Classification: Formulation and Max Likelihood FCML 5.1-5.2.1.6, BRML 10.1-10.2
M 4/13 Bayesian Naive Bayes I: Posterior Inference FCML 5.2.1.7, BRML 10.3 “Midterm” Distributed
W 4/15 Bayesian Naive Bayes II: Prediction and Classification
F 4/17 Bayesian Regression I: Posterior Inference FCML 3.8, BRML 12.1-12.4
M 4/20 Bayesian Regression: Prediction and Model Selection FCML 3.9, BRML 12.1-12.4 “Midterm” Due
  [PART V: Unsupervised Learning and Latent Variable Models (~2 weeks)]  
W 4/22 Clustering: K-Means FCML Ch. 6.1-6.2
F 4/24 Clustering: Mixture Models FCML 6.3.1-6.3.2, BRML 20.1
M 4/27 Clustering: The EM Algorithm FCML 6.3.3-6.3.8, BRML 20.2-20.3 [HW4: Bayesian Classification and Regression due
W 4/29 Clustering: Gibbs Sampling FCML 6.3.9, 9.1-9.2, 10.1
F 5/01 Hidden Markov Models: Overview BRML 23.1-23.3, 23.5
M 5/04 HMMs: Prediction and Inference  
W 5/06 HMMs: Learning with EM  
F 5/08 HMMs: Bayesian Learning  
W 5/13 9pm HW5: Clustering due