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Recommended lectures, literature on math and computer science

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My persional favourite collection of literature on math and computer science

Table of contents

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

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