Machine Learning I

Course goals

This aim of this course is to introduce students to the basic principles of machine learning, as well as several of the most common models and algorithms used in machine learning. The main focus of the course will be on supervised machine learning, but some introduction to unsupervised learning methods like clustering (K-means) will also be provided. Additionally, the course also aims to cover the mathematical concepts most relevant for machine learning, in particular probability theory, linear algebra, and differential and integral calculus required for this course as well as follow-up courses in machine learning.

Content

This course will introduce students to the basic principles of machine learning  as well as several of the most common models and algorithms used in machine learning. The course will begin with linear models (classification and regression), ending with more modern methods like artificial neural networks. The main focus of the course will be on supervised machine learning, but some introduction to unsupervised learning methods like clustering (K-means) will also be provided.  Additionally, the course will  cover the mathematical concepts most relevant for machine learning, in particular probability theory, linear algebra, and differential and integral calculus required for this course as well as follow-up courses in machine learning. 
 
Topics: basics concepts of machine learning, K-nearest neighbours, perceptron, linear classification and regression, logistic regression, feedforward neural networks, concepts related to overfitting and validation, gradient descent. 

 

Additional information

Please note: the time slot shown here is not yet final and may still be modified until the 3rd Wednesday in September.