Week 1 
Jan 25 
Lecture 1: Course outline and Overview of Mini Projects:  Autonomous Vehicle (AV) Safety Analytics
 Healthcare analytics
 TBD
Overview of key data analytics and ML concepts. Slides 


Jan 27 
Lecture 2: Probability Basics Overview, Pvalues, Hypothesis Testing, Fitting Distributions (KS test, KL divergence). Slides 
HW0 release 
Week 2 
Feb 1 
Lecture 3: Miniproject 1 descriptions and task specifications; Inclass Activity 1 (Probability concepts, hypothesis testing, jupyter notebook) ICA1,ICA1_sol, Slides 
MP1 release 

Feb 3 
Lecture 4: Naïve Bayes, conditional independence Slides 
HW0 due, HW1 release 
Week 3 
Feb 8 
Lecture 5: Bayesian Networks Slides 
MP1 checkpoint 

Feb 10 
Lecture 6: Bayesian networks continue, Inclass Activity 2 on Bayesian Networks ICA2, ICA2_sol, Slides. 
HW1 due 

Feb 12 
— 
Discussion Section on Bayesian Network Slides 
Week 4 
Feb 15 
Lecture 7: Clustering: Kmeans, GMM Expectation Maximization Slides 


Feb 17 
Break day 


Feb 19 
— 
Grad project propose ideas 
Week 5 
Feb 22 
Lecture 8: Clustering: GMM, EM continue Slides 
MP1 final checkpoint due 

Feb 24 
Lecture 9: Linear and Nonlinear regression Slides 


Feb 26 
— 
Grad project proposal due 
Week 6 
Mar 1 
Lecture 10: Miniproject 2: Introduction to Healthcare Domain; Guest Lecture Slides 
MP2 release 

Mar 3 
Lecture 11: Principal Component Analysis (PCA) Slides 

Week 7 
Mar 8 
Lecture 12: Inclass Activity 3 on PCA and Clustering 


Mar 10 
Lecture 13: Markov models, and hidden markov models (HMM) 
MP2 checkpoint 
Week 8 
Mar 15 
Lecture 14: Inclass Activity for Midterm revision 


Mar 17 
Lecture 15: Midterm 


Mar 19 
— 
Grad project checkpoint 1 
Week 9 
Mar 22 
Lecture 16: HMM continue 


Mar 24 
Break Day 

Week 10 
Mar 29 
Lecture 17: Factor graphs 
MP2 final checkpoint due 

Mar 31 
Lecture 18: Inclass Activity 4 on HMM 

Week 11 
Apr 5 
Lecture 19: Factor graphs continue, introduction to Miniproject 3 
MP3 release 

Apr 7 
Lecture 20: Belief Propagation 


Apr 9 
— 
Grad project checkpoint 2 
Week 12 
Apr 12 
Lecture 21: Samplingbased methods 


Apr 14 
Lecture 22: Inclass Activity 5 on Factor Graphs 


Apr 16 
— 
MP3 checkpoint 1 
Week 13 
Apr 19 
Lecture 23: Support vector machines, neural networks 


Apr 21 
Lecture 24: Decision trees, random forests, and cross validation 

Week 14 
Apr 26 
Lecture 25: Reinforcement learning 
Grad project final presentation 

Apr 28 
Lecture 26: Recent themes in machine learning 


Apr 30 
— 
MP3 Submission 
Week 15 
May 3 
Lecture 27: Recent themes in machine learning 


May 5 
Lecture 28: Solved examples 
Grad project final submission 

May 6 
Reading Day 

Week 16 
May 13 
Final Exam (7:00  10:00 pm) 
