My persional favourite collection of literature on math and computer science

Table of contents

• Computer Vision
• Linear Algebra
• Probability and Statistics
• Combinatorics
• Deep Learning
• Data Science
• Optimization
• Computer Graphics
• Differential Geometry
• Calculus and Analysis

Computer Vision

Lectures

Lectures

Lecturer(s)	Title	Links	Comments
Prof Fred Hamprecht (UniHeidelberg)	Computer Vision Foundations Class	YouTube,[Prof Fred Hamprecht website] (https://hci.iwr.uni-heidelberg.de/people/fhamprec)	Comment later on
Prof. Faisal Qureshi	Computer Vision	Course WebSite	Computer Vision Basics with Python implementation
Prof. Kris Kitani	Computer Vision (Carnegie Mellon)	Course Website	Lecture notes from the course
Dr. Mubarak Shah	Computer Vision(2012)	YouTube
Prof. Olga Russakovsky (Princeton University)	Computer Vision	LectureNotes	Olga Russakovsky is one of the authors of ImageNet Challenge

Linear Algebra

Literature

Authors	Title	Comments
Lloyd N. Trefethen, David Bau III	Numerical Linear Algebra	Recommended by Gilbert Strang(MIT)
Gene H. Golub, Charles F. Van Loan	Matrix Computation	Recommended by Gilbert Strang(MIT)
David Poole	Linear Algebra: A Modern Introduction	AUA textbook
Michael Artin	Algebra: Second Edition	First chapter is the proper way to introduce the subject

Lectures

Lecturer(s)	Title	Links	Comments
Gilbert Strang(MIT)	Linear Algebra	MIT Lecture	Course offered by MIT

Probability and Statistics

Lecturer(s)	Title	Links	Comments
Joe Blitzstein(Harvard)	Statistics 110: Probability	YouTube, Lecture Course	The course provides a solid introduction to probability theory and ,what is also interesting, into probability history. The professor does a great job in explaining/telling the historical origins of many examples given in the course.

Combinatorics

Literature

Authors	Title	Comments
Herbert S. Wilf[University of Pennsylvania]	generatingfunctionology	Very easy intro and a nice set of excercises

Calculus and Analysis

Literature

Authors	Title	Comments
Spivak, Michael	Calculus	Nice introductory course on calculus

Deep Learning

Lectures

Lecturer(s)	Title	Links	Comments
Dmitry Vetrov (HSE, Samsung AI) and many others	Deep Bayes Summer Camp	YouTube , Github	Course offered by Scholtech Faculty and the leading russian ML researchers

Data Science

Lectures

Lecturer(s)	Title	Links	Comments
Ali Ghodsi	Data Visualization	YouTube	Course offered by The University of Waterloo
Alexander Novikov, Daniil Polykovskiy (Higher School of Economics)	Bayesian Methods for Machine Learning	Coursera	Course offered at HSE, nice explanation of EM algorithm
Dmitriy Vetrov	Bayesian Methods for Machine Learning (Russian Language)	Youtube	The course has videos from lectures and seminars
John Paisley(Columbia)	Bayesian models for machine learning*	Lecture Notes	The lecture notes were recommended by Deep Bayes summer camp of Dmitriy Vetrov

Optimization

Lectures

Lecturer(s)	Title	Links	Comments
Michel Bierlaire(EPFL)	Descent methods	YouTube	Very nice explanation of Arminjo conditions
Ryan Tibshirani (Carnegie Mellon University)	Convex Optimization: Fall 2019	YouTube , CMU Course	Review Later

Literature

Authors	Title	Comments
Dimitris Bertsimas	Introduction to Linear Optimization	The main textbook used in most of the courses, also a great intuitive explaination of Simplex algorithm

Computer Graphics

Literature

Lecturer(s)	Title	Links	Comments
Dr. C.-K. Shene(Michigan Technological University)	Introduction to Computing with Geometry	Lecture Notes	Nice Section on B-Spline/Bezier Curves

Differential Geometry

Lectures

Lecturer(s)	Title	Links	Comments
Дынников И. А (Профе́ссор Росси́йской акаде́мии нау́к)	Классическая дифференциальная геометрия	YouTube,Lecture Notes	Recommeded Literature: Manfredo p. do carmo Differential Geometry of curves and surfaces, Норден А.П. Краткий курс дифференциальной геометрии

Literature

Authors	Title	Comments
Attila M´at´e (Brooklyn College of the City University of New York)	The Frenet–Serret formulas	Nice and clear derivation of Frenet-Serret formulas