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Aug 22 |
Lecture 1: Course outline and Overview of Mini Projects: - Autonomous Vehicle (AV) Safety Analytics
- Healthcare analytics
- System analytics
Overview of key data analytics and ML concepts. Slides |
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Aug 24 |
Lecture 2: Probability Basics Overview, Probability and Hypothesis Testing, P-values , Fitting Distributions (KS test, KL divergence); Introducing Mini-project; Demostrating AV Simulator with Carla Slides Demo |
HW0 release sol |
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| 2 |
Aug 29 |
Lecture 3: In-class Activity 1 (Probability concepts, hypothesis testing, jupyter notebook) link sol |
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MP1 release part1 doc client sol |
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Aug 31 |
Lecture 4: Overview of unsupervised clustering algorithms: K means, EM, and GMM among others. Slides |
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| 3 |
Sep 5 |
No classes – Labor Day |
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Sep 7 |
Lecture 5: Overview of regression and feature transformation techniques (PCA, Factor analysis, and distance metrics) Slides |
HW0 due |
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| 4 |
Sep 12 |
Lecture 6: Real world examples of feature transformationSlides |
HW1(sol included) release |
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Sep 14 |
Lecture 7: In class activity on feature transformation link sol |
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Sep 16 |
Discussion 1: session on MP1 (optional) |
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Sep 18 |
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MP1 checkpoint 1 due slide template Full doc |
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| 5 |
Sep 19 |
Lecture 8: Introduction to Naïve Bayes, conditional independence Slides |
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Sep 21 |
Lecture 9: Bayesian Networks Slides |
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Sep 23 |
Discussion 2: session on MP1 and Baysian network(time permitting) (optional) |
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| 6 |
Sep 26 |
Discussion section on Bayesian Network |
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Sep 27 |
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MP1 final checkpoint dueslide template |
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Sep 28 |
Lecture 10: Bayesian networks continue, In-class Activity 3 on Bayesian Networks link sol |
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Sep 30 |
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Project proposal due doc |
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| 7 |
Oct 3 |
Lecture 11: Mini-project 2: Application of Bayesian Networks/PGMs to Health-care Domain; Slides |
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MP2 releaselink |
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Oct 5 |
Lecture 12: Hidden Markov Models (HMM) Slides |
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HW2 Released link sol |
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| 8 |
Oct 10 |
Lecture 13: Hidden Markov Models continued Slides |
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Oct 12 |
Lecture 14: In-class Activity for Midterm revision link sol |
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HW2 due |
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| 9 |
Oct 17 |
Lecture 15: Midterm sol |
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Oct 19 |
Lecture 16: Factor Graphs & Belief Propagation Slides |
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Oct 21 |
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MP2 Checkpoint 1 |
| 10 |
Oct 24 |
Lecture 17: Factor Graphs & Belief Propagation continued ; Slides |
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Oct 26 |
Lecture 18: In-class Activity 5 on HMM link sol |
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HW3 released link sol |
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| 11 |
Oct 31 |
Lecture 19: Approximate Inference Methods slides |
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MP2 final checkpoint due |
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Nov 2 |
Lecture 20: Guest Lecture |
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Nov 5 |
Discussion: factor graph Slides |
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MP3 CP1 release link |
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| 12 |
Nov 7 |
Lecture 21: Introduction to MP3 slides |
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Nov 9 |
Lecture 22: In-class Activity 6 on Factor Graphs sol |
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| 13 |
Nov 14 |
Lecture 23: Sampling-based Methods and SVMs |
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Nov 16 |
Lecture 24: Neural networks slides |
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MP3 checkpoint 1 |
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| 14 |
Nov 21 |
No classes – Fall break |
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Nov 23 |
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HW4 release link sol ; MP3 Full release link |
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| 15 |
Nov 28 |
Lecture 25: Reinforcement Learning and solution techniques via Partially Observable Markov Decision Processes RL slides Actor-Critic Alzheimers |
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Nov 30 |
Lecture 26: Real world Cloud Datacenter-based example of combining RL with PGMs |
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Dec 2 |
Discussion 5: session on neural network (optional) Slides |
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| 16 |
Dec 5 |
Lecture 27: Grad Project Presentation slide template |
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HW4 submission |
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Dec 7 |
*Lecture 28:*ICA 7: RL and final review sol) |
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past exam with sol |
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Dec 8 |
No classes – Reading Day |
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Dec 16 |
1:30 ~ 4:30 Final Exam |
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