Spring 2020 - Ani Adhikari
Spring 2020 - Ani Adhikari
| Week | Date | Content | Prep & Assignments |
|---|---|---|---|
| 1 | Tues 1/21 | Lecture 1: Probability — axioms, rules, approximation |
Weeks 1 & 2 Guide Lab 1: Simple Random Samples (Due 1/26 at 12:00PM noon) HW 1 (Due 1/28 at 12:00PM noon) |
| Wed 1/22 | Discussion | ||
| Thurs 1/23 | Lecture 2: Random variables — distributions, equality, conditioning | ||
| Fri 1/24 | Discussion | ||
| 2 | Tue 1/28 | Lecture 3: Symmetry and collections of events |
Weeks 1 & 2 Guide Lab 2:Total Variation (Due 2/2 at 12:00PM noon) HW 2 (Due 2/4 at 12:00PM noon) |
| Wed 1/29 | Discussion | ||
| Thu 1/30 | Lecture 4: Binomial and related counts; a Poisson limit | ||
| Fri 1/31 | Discussion | ||
| 3 | Tue 2/4 | Lecture 5: Independence and Poissonization |
Weeks 3 & 4 Guide Lab 3: Expectation of Discrete Order Statistics (Due 2/9 at 12:00PM noon) HW 3 (Due 2/11 at 12:00PM noon) |
| Wed 2/5 | QUIZ 1 | ||
| Thu 2/6 | Lecture 6: Expectation | ||
| Fri 2/7 | Discussion | ||
| 4 | Tue 2/11 | Lecture 7: Expectation and additivity |
Weeks 3 & 4 Guide Lab 4: Success Runs and Other Patterns (Due 2/23 at 12:00PM noon) HW 4 (Due 2/18 at 12:00PM noon) |
| Wed 2/12 | Discussion | ||
| Thu 2/13 | Lecture 8: Expectation by conditioning | ||
| Fri 2/14 | Discussion | ||
| 5 | Tue 2/18 | Lecture 9: Long run behavior of Markov Chains |
Weeks 5 & 6 Guide Lab 4: Success Runs and Other Patterns (Due 2/23 at 12:00PM noon) Homework 5 (Due 2/25 at 12:00PM noon) |
| Wed 2/19 | Discussion | ||
| Thu 2/20 | Lecture 10: Balance | ||
| Fri 2/21 | Discussion | ||
| 6 | Tue 2/25 | Lecture 11: Markov Chain Monte Carlo |
Weeks 5 & 6 Guide Midterm Contents Lab 5: Codebreaking by MCMC (Due 3/1 at 12:00PM noon) |
| Wed 2/26 | Discussion | ||
| Thu 2/27 | Lecture 12: Examples as review | ||
| Fri 2/28 | Discussion | ||
| 7 | Tue 3/3 | MIDTERM |
Homework 6 (Due 3/10 at 12:00PM noon) Weeks 7-9 Guide |
| Wed 3/4 | No Discussion | ||
| Thu 3/5 | Lecture 13: SD, tail bounds | ||
| Fri 10/11 | Discussion | ||
| 8 | Tue 3/10 | Covariance and its uses (Part 1) (Part 2) (Part 3) (Part 4) |
Weeks 7-9 Guide Homework 7 (Due 3/20 at 12:00PM noon) Lab 6: Decisions Based on Ranks (Due 3/20 at 12:00PM noon) |
| Wed 3/11 | Discussion | ||
| Thu 3/12 | Central Limit Theorem (Part 5) (Part 6) (Part 7) (Part 8) |
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| Fri 3/13 | Discussion | ||
| 9 | Tue 3/17 | Probability densities (Part 1) (Part 2) (Part 3) (Part 4) |
Weeks 7-9 Guide Homework 8 (Due 3/31 at 12:00PM noon) Lab 7: Densities and SymPy (Due 4/5 at 12:00PM noon) |
| Wed 3/18 | Discussion | ||
| Thu 3/19 | Transformations (Part 5) (Part 6) (Part 7) |
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| Fri 3/20 | Discussion | ||
| Tue 3/24 | |||
| Wed 3/25 | |||
| Thu 3/26 | |||
| Fri 3/27 | |||
| 10 | Tue 3/31 | Joint densities (Part 1) (Part 2) (Part 3) (Part 4) (Part 5) (Part 6) (Part 7) |
Homework 9 (Due 4/7 at 12:00PM noon) Week 10 Checkpoint |
| Wed 4/1 | Problem Solving Sessions (Videos) |
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| Thu 4/2 | Lecture 19: Independent normal and gamma variables | ||
| Fri 4/3 | Problem Solving Sessions | ||
| 11 | Tue 4/7 | Lecture 20: Moment generating functions; Chernoff bound | |
| Wed 4/8 | Problem Solving Sessions | ||
| Thu 4/9 | Lecture 21: MLE; the beta family | ||
| Fri 4/10 | Problem Solving Sessions | ||
| 12 | Tue 4/14 | Lecture 22: Conditioning; MAP estimates | |
| Wed 4/15 | Problem Solving Sessions | ||
| Thu 4/16 | Lecture 23: The beta and the binomial | ||
| Fri 4/17 | Problem Solving Sessions | ||
| 13 | Tue 4/21 | Lecture 24: Prediction and error | |
| Wed 4/22 | Problem Solving Sessions | ||
| Thu 4/23 | Lecture 25: Random vectors; multivariate normal | ||
| Fri 4/24 | Problem Solving Sessions | ||
| 14 | Tue 4/28 | Lecture 26: Correlation and simple regression | |
| Wed 4/29 | Problem Solving Sessions | ||
| Thu 4/30 | Lecture 27: Multiple regression | ||
| Fri 5/1 | Problem Solving Sessions | ||
| 16 | Tue 5/5 | ||
| 17 | Fri 5/15 | FINAL EXAM (7PM to 10PM) | |