sinamajidian / mfb

Mathematical Foundations of Bioinformatics

Overview

• Instructor: Hesam Montazeri (hesam.montazeri at ut.ac.ir)
• Teaching Assistant: Sajedeh Bahonar (sajedeh.bahonar at ut.ac.ir)
• Time & Location: Nov 2020 to Jan 2021 (online lectures)

Textbooks

• [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.

Exam

• Fall 2020

Lecture Schedule

#	Lecture	Reading Assignments	Homeworks
1	Lecture 1- Introduction to probability; counting; Birthday paradox; story proof (slides, video) Lecture 2- probability axioms; inclusion-exclusion principle (slides, video) Tutorial 1- On Karyotyping by Sajedeh Bahonar (video)	Required: ITP, Ch. 1	HW1
2	Lecture 3- conditional probability; law of total probability; Bayes' rule; Monty Hall Problem (slides, video) Lecture 4- conditional probability, continued (slides, video) Tutorial 2- Lollipop plot by Sajedeh Bahonar (video)	Required: ITP, Ch. 2	HW2
3	Lecture 5- Gambler’s ruin; Simpson’s paradox; Random Variables (slides, video) Lecture 6- solutions to the selected exercises (no slides available); Bernoulli and Binomial distributions (slides, video)	Required: ITP, Ch. 3	HW3
4	Lecture 7- Hypergeometric and Uniform distributions (slides, video) Lecture 8- CDF, functions of RVs; more on independence (slides, video) Tutorial 3- TCGA datasets (video)	Required: ITP, Ch. 3	HW4
5	Lecture 9- Expectation; Geometric and NB distributions; linearity of expectation (slides, video) Lecture 10- Indicator RVs; fundamental bridge; LOTUS (slides, video) Tutorial 4- Shiny apps by Sajedeh Bahonar (video)	Required: ITP, Ch. 4	HW5
6	Lecture 11- LOTUS; Variance; Poisson distribution (slides, video) Lecture 12- Poisson distribution (slides, video) Tutorial 5- Introduction to dynamic programming by Fatemeh Vafaee (video)	Required: ITP, Ch. 4	HW6
	Review week
7	Lecture 13- Continuous random variables (slides, video) Tutorial 6- Apply family in R; Dot plot by Sajedeh Bahonar (video)	Required: ITP, Ch. 5	HW7
8	Lecture 14- Normal distribution (slides, video) Lecture 15- Exponential distribution (slides, video) Lecture 16- Summaries of a distribution; moment generating functions (slides, video)	Required: ITP, Ch. 5-6	HW8
9	Lecture 17- solutions to the selected exercises (no slides available); MGF-continued (slides, video) Lecture 18- Joint distributions (slides, video)	Required: ITP, Ch. 6-7	HW9
10	Lecture 19-Covariance, multinomial, multivariate Normal (slides, video)	Required: ITP, Ch. 7	HW10