Self-study and distant teaching

See the list of suggested literature.

During the time period of suspended teaching, we suggest you to watch educational videos from online sources. We suggest the following:

• Andrew Ng's Machine Learning (at Coursera.org)
  • Covers most of the machine learning part of our course.
  • The online course is available for free, currently running session from March 16 to the beginning of June. Sign up to get access to the course contents.

• Pieter Abbeel's and Dan Klein's Artificial Intelligence (at edX.org)
  • Covers many topics of our course (and of the bachelor B3B33KUI).
  • The whole set of lecture videos from 2014 can be found at youtube.

• Patrick H. Winston MIT 6.034 AI course, Fall 2010
  • MIT 6.034 playlist on YouTube

• Jeff Miller's mathematicalmonk youtube channel, and especially the machine learning playlist
  • Covers lots of topics, very useful for some topics in our course.

If you find videos or other materials with a better presentation of the topics, let me know! We can share it!

• Nearest neighbors
  • MIT: Nearest neighbors
  • MathematicalMonk 1.6
• Kernel functions, SVM
  • Coursera ML course, week 7
    • The first part contains description of linear maximal margin classifier and its relation to logistic regression, i.e. something that was already covered in our course.
    • The second part presents the kernel functions (new topic).
  • MIT: SVMs
• Decision trees
  • MathematicalMonk 2.1, 2.2, 2.3, 2.4, 2.5

• Bagging
  • MathematicalMonk: 2.6, 2.7
• Boosting, Adaboost
  • Trevor Hastie: Gradient Boosting
  • MIT: Boosting
• Random forests
  • MathematicalMonk: 2.8