General Information

Course Outline

The course is a basic introduction to machine learning, including:

  • PAC: Generalization bounds
  • PAC: VC bounds
  • Perceptron
  • Support Vector Machine (SVM)
  • Kernels
  • Stochastic Gradient Descent
  • Boosting
  • Decision Trees
  • Regression
  • Generative Models, EM
  • Clustering, k-means
  • PCA

The course will include both theory and applied machine learning,
and a special emphasis will be put on machine learning algorithms.


Location and Hours

Please check the course schedule.

Staff|ruosnam#ruosnaM yahsiY .forP (homepage)|rimag#nosrebolG rimA .forP (homepage)

Teaching Assistants:|regiewhcs#regiewhcS vegeR (homepage)
Feel free to coordinate reception hours with any of us via email.


Final Grade is made out of:

  • 80% Exam
  • 20% Exercises

As always, one has to pass the exam in order to pass the course.

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