D:\Inetpub\shared\yedion\syllabus\03\2018\0368\0368323501_desc.txt Course description 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.
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