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.
Formalities
Location and Hours
Please check the course schedule.
Staff
li.ca.uat|ruosnam#ruosnaM yahsiY .forP (homepage)
li.ca.uat.tsop|rimag#nosrebolG rimA .forP (homepage)
- Teaching Assistants:
li.ca.uat.tsop|regiewhcs#regiewhcS vegeR (homepage)
Feel free to coordinate reception hours with any of us via email.
Grade
Final Grade is made out of:
- 80% Exam
- 20% Exercises
As always, one has to pass the exam in order to pass the course.