- Instructor: Alireza Fotuhi (ar.fotuhi at gmail.com) & Hesam Montazeri (hesam.montazeri at ut.ac.ir)
- Teaching Assistants: Marzieh Gholami (at ut.ac.ir)
- Time & Location: Sundays and Tuesdays 10:00-12:00 at Ghods st. 37, Department of Bioinformatics, IBB, Tehran.
- [ITP] Blitzstein, Joseph K., and Jessica Hwang. Introduction to probability. Crc Press, 2019 (download)
- [OpI] Diez, David M., Christopher D. Barr, and Mine Cetinkaya-Rundel. OpenIntro statistics. OpenIntro, Fourth Edition, 2019.
- [MML] Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong. Mathematics for machine learning. Cambridge University Press, 2020.
- [DMA] Rosen, Kenneth H., and Kamala Krithivasan. Discrete mathematics and its applications: with combinatorics and graph theory. Tata McGraw-Hill Education, 2012.
- [MSDA] John A. Rice, Mathematical Statistics And Data Analysis, Third edition, 2007.
- [PRML] Pattern Recognition and Machine Learning by Christopher Bishop, 2006.
| Lecture | Reading Assignments |
|---|---|
| Lecture A1- Introduction to probability; counting; Birthday paradox; story proof; probability axioms; inclusion-exclusion principle (slides, video A1) | Required: ITP, Ch. 1 |
| Lecture A2- conditional probability (slides) Conditional probability; Two children problem (video A2-part 1) Bayes’ rule; the law of total probability; Random coin problem; Testing for a rare disease (video A2-part 2) Bayes' rule with extra conditioning, independenc; coherency of Bayes' rule (video A2-part 3) Monty Hall problem, Simpson’s paradox, Gambler’s ruin (video A2-part 4) | Required: ITP, Ch. 2 |
| Lecture A3- random variables and their distributions (slides) Random variables; PMF (video A3-part 1) Bernoulli and Binomial distributions (video A3-part 2) Hypergeometric distribution (video A3-part 3) Discrete Uniform distribution; Random slips of paper example (video A3-part 4) Cumulative distribution function; independence of random variables (video A3-part 5) Conditional independence; Fisher exact test (video A3-part 6) | Required: ITP, Ch. 3 |
| Lecture A4- Expectation (slides) Expectation; linearity of expectation (video A4-part 1) Geometric and negative binomial distributions (video A4-part 2) Indicator Random Variables (video A4-part 3) LOTUS; Variance (video A4-part 4) Poisson distribution (video A4-part 5) Poisson approximation; Poisson & Binomial relationship (video A4-part 6) | Required: ITP, Ch. 4 |
| Lecture A5- Continuous random variable (slides) Introduction to continuous random variable (video A5-part 1) Normal distribution (video A5-part 2) Exponential distribution; Poisson process (Video A5-part 3) Exponential distribution-continued (video A5-part 4) | Required: ITP, Ch. 5 |
| Lecture A6- Moments (slides) Summaries of a distribution (video 6-part 1) Moment generating functions (video A6-part 2) | Required: ITP, Ch. 6 |
| Homeworks | Deadline | Tutorial |
|---|---|---|
| HW_P1 | Mehr 6, 1401 | Introduction to R, RStudio; Basic plotting functions in R By Sajedeh Bahonar (video) |
| HW_P2 | Aban 11, 1401 | Oncoplots |
| Textbook | Chapter | Homeworks |
|---|---|---|
| ITP | Ch. 1 | 10, 11, 14, 18, 40, 49, 60 |
| ITP | Ch. 2 | 3, 37, 60, 62, 68, 73 |
| ITP | Ch. 3 | 1, 3, 8, 14, 19, 22, 26, 27, 30, 31, 34, 38 |
| ITP | Ch. 4 | 7, 16a, 26, 31, 33, 39, 49, 53, 54, 69, 71, 89, 91 |
| ITP | Ch. 5 | 1, 5, 8, 14, 22, 28, 40a, 41, 44, 45, 58, 61, 62ab |
| ITP | Ch. 6 | 1, 15, 17, 22 |
| OpI | Ch. 1 | 2, 4, 8, 10, 22, 30, 34, 38, 40, 42 |
| OpI | Ch. 2 | 2, 6, 10, 14, 16, 22, 28, 34 |
| OpI | Ch. 5 | 4, 6, 8, 10, 12 |
| MSDA | Ch. 7 | 67 |
| MSDA | Ch. 8 | 7a-c, 45 |