Short Course Description
This course provides a basic introduction to machine learning (namely the field of computer science studying algorithms that learn from examples). This field underlies modern applications of AI, including machine vision, natural language processing, autonomous driving and others.
The course will provide the theoretical foundations for understanding learning algorithms, describe different algorithms, and provide practical experience.
Some of the topics studies are:
+ Supervised learning: PAC learning, VC dimension, perceptron, SVM, stochastic gradient descent, boosting, deep learning and decision trees
+ Unsupervised learning: principal component analysis, clustering, EM algorithm
The course comprises both theory and practice, with special emphasis on different machine learning algorithms. Theoretical topics are mathematical in nature; practical exercises are based on Python.
Full Syllabus
