What is active learning in machine learning

Machine learning 2

Lecture and exercise in the summer semester 2010. Prof. Tobias Scheffer, Dr. Niels Landwehr, Christoph Sawade.

Events

The course comprises 4 hours per week (6 CP); creditable for diploma, bachelor and master.

  • Exercise: Wednesday, 10: 15-11: 45, 03.04.0.02 (from 05.05.2010).
  • Lecture: Wednesday, 12: 00-13: 30, 03.04.0.02.

Content

The event complements and deepens the lecture "Machine Learning". Machine learning deals with algorithms that can learn from data. Machine learning algorithms extract models from data, which can then be used to make predictions about the observed system. Applications for data analysis methods range from the prediction of credit risks to the evaluation of astronomical data to personal music recommendations. The content covered in this second part of the course includes reinforcement learning, graphical models, Gaussian processes, kernel methods, active learning and other topics.

literature

Chris Bishop. Pattern Recognition and Machine Learning. We have ordered 30 copies to lend.

Lecture blog, slides.

Comment on the lecture and ask questions!

Exercises

Tasks of the 1st exercise on May 5th, 2010

Exercises of the 2nd exercise on May 12, 2010

Tasks of the 3rd exercise on May 19, 2010

Tasks of the 4th exercise on May 26, 2010

Exercises of the 5th exercise on June 2nd, 2010

Exercises of the 6th exercise on June 9th, 2010

Tasks of the 7th exercise on June 16, 2010

Exercise on June 23, 2010 is omitted (VL takes place as usual)!

Tasks of the 8th exercise (will be evaluated together with exercise 9 on July 14, 2010)

Tasks and resources of the 9th exercise on July 14, 2010

Tasks of the 10th exercise on July 21, 2010