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Catalogue of Good Courses and Books for Subjects Related to Machine Learning

Graphics

Courses

  1. http://cs233.stanford.edu/ (Leo Guibas)
  2. http://graphics.stanford.edu/courses/cs268-16-fall/ (Leo Guibas)
  3. http://groups.csail.mit.edu/gdpgroup/6838_spring_2019.html (Justin Solomon)
  4. https://cse291-i.github.io/ (Hao Su)
  5. http://ddg.cs.columbia.edu/SGP2014/LaplaceBeltrami.pdf
  6. https://graphics.stanford.edu/courses/cs468-13-spring/schedule.html (Differential Geometry for Computer Science)
  7. https://cosmolearning.org/video-lectures/differential-geometry-curves/ (Justin Solomon)
  8. Computer graphics course by Keenan: https://www.youtube.com/playlist?list=PL9_jI1bdZmz2emSh0UQ5iOdT2xRHFHL7E

Books

  1. http://web.mit.edu/hyperbook/Patrikalakis-Maekawa-Cho/node2.html
  2. http://graphics.stanford.edu/courses/cs205a/ (Justin Solomon)
  3. http://www.pmp-book.org/

Tutorial

  1. https://www-users.cs.umn.edu/~saad/PDF/umsi-2009-31.pdf
  2. https://github.com/timzhang642/3D-Machine-Learning#3d_synthesis

Differential Geometry

Books

  1. Differntial Geometric of Curves and Surfaces (Do Carmo): https://www.slader.com/textbook/9780132125895-differential-geometry-of-curves-and-surfaces/88/exercises/1/
  2. Elementary Differential Geometry (Andrew Pressley)
  3. Non-Euclidean Methods in Machine Learning (http://graphics.stanford.edu/courses/cs468-20-fall/index.html)

Course

  1. https://sites.math.rutgers.edu/~zchan/432/info.html (Do Carmo)
  2. http://faculty.bard.edu/~belk/math352f11/ (Do Carmo)
  3. https://www.math.upenn.edu/~shiydong/spring12.html (Do Carmo)
  4. http://web.math.ucsb.edu/~shoseto/teaching/147A/ (Andrew Pressley)
  5. http://brickisland.net/DDGSpring2016/ (Discrete Differential Geometry at CMU in Spring 2016)
  6. https://graphics.stanford.edu/courses/cs468-13-spring/schedule.html
  7. http://www.supermath.info/DifferentialGeometry.html (James cook)

Optimization

Courses

  1. https://laurentlessard.com/teaching/524-intro-to-optimization/ (Wisconsin Madison)
  2. http://www.stat.cmu.edu/~ryantibs/convexopt-S15/
  3. https://optimization.discovery.wisc.edu/graduate-studies/optimization-courses/
  4. https://laurentlessard.com/teaching/532-matrix-methods/
  5. http://graphics.stanford.edu/courses/cs468-20-fall/schedule.html

Books

  1. Numerical Optimization (Nocedal)
  2. Convex Optimization (Boyd)
  3. Optimization Models (Calafiore, Ghaoui)

Statistics

Books

  1. Statistical Inference (Casella, Berger)

Courses

Linear Algebra

Courses

  1. MIT OCW, Gilbert Strang https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/index.htm
  2. Stanford, Linear Dynamical System https://see.stanford.edu/Course/EE263
  3. https://www.youtube.com/watch?v=Ikl1wnwIOmM&list=PL_a9tY9IhJuPDEDq97tq0uKXpsTZYBIXe&ab_channel=LadislavKavan
  4. https://github.com/oseledets/nla2020 [Numerical Linear Algebra]

Books

  1. Introduction to Linear Algebra, Gilbert Strang
  2. Linear Algebra Done Right, Sheldon Axler

Calculas

Courses

  1. http://tutorial.math.lamar.edu/Classes/CalcIII/CalcIII.aspx
  2. https://ocw.mit.edu/courses/mathematics/18-02-multivariable-calculus-fall-2007/
  3. https://www.khanacademy.org/math/multivariable-calculus (good visualization)

Books

  1. Calculas, Thomas Finney

PGM

Course

  1. http://cs.brown.edu/courses/cs242/

Books

  1. Kevin Murphy
  2. CM Bishop

Reinforcement Learning

Course

  1. Phil Thomas course https://people.cs.umass.edu/~pthomas/courses/CMPSCI_687_Fall2019.html

Books

  1. http://incompleteideas.net/book/the-book-2nd.html

Differential Equations

Course

  1. https://ocw.mit.edu/resources/res-18-009-learn-differential-equations-up-close-with-gilbert-strang-and-cleve-moler-fall-2015/index.htm
  2. https://ocw.mit.edu/courses/mathematics/18-303-linear-partial-differential-equations-fall-2006/index.htm

Books

  1. Advanced Engineering Mathematics, Erwin Kreyszig.

Machine Learning

Courses

  1. https://www.cs.cornell.edu/courses/cs4780/2018fa/page18/
  2. (Kernel Methods in Machine Learning)[http://members.cbio.mines-paristech.fr/~jvert/svn/kernelcourse/course/2021mva/index.html]

Books

Mathematics for Machine Learning and Graphics

Courses

  1. [Shape analysis (spring 2019), Justin Solomon](http://groups.csail.mit.edu/gdpgroup/6838_spring_2019.html)
  2. [Computer Graphics (Fall 202), Keenan Krane](http://15462.courses.cs.cmu.edu/fall2020/)
  3. [CS 468: Differential Geometry for Computer Science (spring 2013)](https://www.youtube.com/playlist?list=PLQ3UicqQtfNvPmZftPyQ-qK1wdXBxj86W)
  4. [Symposium of Graphics Proessing](https://www.youtube.com/playlist?list=PLUykN3u3Z3NXLOeaUJmZHdEJ67KvpNzK2)
  5. Tutorial on libgl with python: https://mybinder.org/v2/gh/libigl/libigl-python-bindings/master?filepath=tutorial%2Ftutorials.ipynb
  6. https://github.com/Hippogriff/smgp (geometry modeling course and solution from ETH https://github.com/eth-igl/GP2018-Assignments)
  7. https://github.com/eth-igl/GP2020-Assignments
  8. https://github.com/danielepanozzo/gp
  9. https://github.com/alecjacobson/geometry-processing-parameterization (This seems to me the best geometry processing coursework.)

Books

Computer vision

courses

  1. [First principles of computer vision](https://www.youtube.com/channel/UCf0WB91t8Ky6AuYcQV0CcLw?app=desktop)
  2. [Multi-view computer vision](https://www.youtube.com/playlist?list=PLEB45naDUsF2vpvdxZ72Jjl8ZEKISv4Nh)
  3. [CS231A: Computer Vision, From 3D Reconstruction to Recognition](https://web.stanford.edu/class/cs231a/)