====== Self-study and distant teaching ======
See the list of [[courses:ui:literature|suggested literature]].

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

  * [[https://www.coursera.org/instructor/andrewng|Andrew Ng]]'s [[https://www.coursera.org/course/ml|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.

  * [[https://people.eecs.berkeley.edu/~pabbeel/|Pieter Abbeel]]'s and [[https://people.eecs.berkeley.edu/~klein/|Dan Klein]]'s [[https://www.edx.org/course/artificial-intelligence-uc-berkeleyx-cs188-1x-0#.VOslTy4mfm4|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 [[https://www.youtube.com/playlist?list=PLNozK-HB4MXsVAN6cqkCAO09RChbIAk5i|youtube]].

  * Patrick H. Winston [[https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/|MIT 6.034 AI course]], Fall 2010
    * [[https://www.youtube.com/watch?v=TjZBTDzGeGg&list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi|MIT 6.034]] playlist on YouTube 

  * [[http://www2.stat.duke.edu/~jwm40/teaching.html|Jeff Miller]]'s [[https://www.youtube.com/user/mathematicalmonk|mathematicalmonk youtube channel]], and especially the [[https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA|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!**

===== Lecture 4 =====
  * **Nearest neighbors**
    * MIT: [[https://www.youtube.com/watch?v=09mb78oiPkA&list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi&index=10|Nearest neighbors]]
    * [[https://www.youtube.com/watch?v=4ObVzTuFivY|MathematicalMonk 1.6]]
  * **Kernel functions, SVM** 
    * [[https://www.coursera.org/learn/machine-learning/home/week/7|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: [[https://www.youtube.com/watch?v=_PwhiWxHK8o|SVMs]]
  * **Decision trees** 
    * MathematicalMonk [[https://www.youtube.com/watch?v=p17C9q2M00Q&list=PLD0F06AA0D2E8FFBA&index=7|2.1]], [[https://www.youtube.com/watch?v=zvUOpbgtW3c&list=PLD0F06AA0D2E8FFBA&index=8|2.2]], [[https://www.youtube.com/watch?v=_RxqyvRK0Rw&list=PLD0F06AA0D2E8FFBA&index=9|2.3]], [[https://www.youtube.com/watch?v=S51plSJBC2g&list=PLD0F06AA0D2E8FFBA&index=10|2.4]], [[https://www.youtube.com/watch?v=UMtBWQ2m04g&list=PLD0F06AA0D2E8FFBA&index=11|2.5]]

===== Lecture 5 =====
  * **Bagging** 
    * MathematicalMonk: [[https://www.youtube.com/watch?v=5Lu1eTiX7qM&list=PLD0F06AA0D2E8FFBA&index=13&t=0s|2.6]], [[https://www.youtube.com/watch?v=JM4Y0B6Ho90&list=PLD0F06AA0D2E8FFBA&index=13|2.7]]
  * **Boosting, Adaboost** 
    * Trevor Hastie: [[https://www.youtube.com/watch?v=wPqtzj5VZus|Gradient Boosting]]
    * MIT: [[https://www.youtube.com/watch?v=UHBmv7qCey4&list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi&index=18|Boosting]]
  * **Random forests**
    * MathematicalMonk: [[https://www.youtube.com/watch?v=o7iDkcpOr_g&list=PLD0F06AA0D2E8FFBA&index=14|2.8]]