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

• Supervised learning (PAC learning, VC dimension, perceptron, SVM, stochastic gradient descent, deep learning, boosting, decision trees)
• Unsupervised learning (principal component analysls, clustering, EM algorithm)

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

See here for lecture notes from last year:

http://ml-intro-2016.wikidot.com/course-schedule

The excercises will be done in Python; The course has large parts that are mathematical in nature.

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

Supervised learning (PAC learning, VC dimension, perceptron, SVM, stochastic gradient descent, deep learning, boosting, decision trees)
Unsupervised learning (principal component analysls, clustering, EM algorithm)
The course will include both theory and applied machine learning,
and a special emphasis will be put on machine learning algorithms.

The excercises will be done in Python; The course has large parts that are mathematical in nature.