Schedule


Week# Date Title Releases and Due Dates
Week 1 Jan 25 Lecture 1: Course outline and Overview of Mini Projects:
  1. Autonomous Vehicle (AV) Safety Analytics
  2. Healthcare analytics
  3. TBD
Overview of key data analytics and ML concepts. Slides
Jan 27 Lecture 2: Probability Basics Overview, P-values, Hypothesis Testing, Fitting Distributions (KS test, KL divergence). Slides HW0 release
Week 2 Feb 1 Lecture 3: Mini-project 1 descriptions and task specifications; In-class 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, In-class 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: K-means, 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 Non-linear regression Slides
Feb 26 Grad project proposal due
Week 6 Mar 1 Lecture 10: Mini-project 2: Introduction to Health-care Domain; Guest Lecture Slides MP2 release
Mar 3 Lecture 11: Principal Component Analysis (PCA) Slides
Week 7 Mar 8 Lecture 12: In-class 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: In-class 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: In-class Activity 4 on HMM
Week 11 Apr 5 Lecture 19: Factor graphs continue, introduction to Mini-project 3 MP3 release
Apr 7 Lecture 20: Belief Propagation
Apr 9 Grad project checkpoint 2
Week 12 Apr 12 Lecture 21: Sampling-based methods
Apr 14 Lecture 22: In-class 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)