Elements of Machine Learning

Summary

In this course we will discuss the foundations – the elements – of machine learning. In particular, we will focus on the ability of, given a data set, to choose an appropriate method for analyzing it, to select the appropriate parameters for the model generated by that method, and to assess the quality of the resulting model. Both theoretical and practical aspects will be covered.

Lectures will start on October 16th!

Prerequisites        

The course is targeted at students in computer science, data science and AI, cybersecurity, bioinformatics, math, and general sciences with a mathematical background. Students should know the basics of programming, proof techniques, linear algebra, and statistics, for example by having taken Programming I and II (for programming), Mathematics for Computer Scientists I and II (for linear algebra), and then either Statistics Lab or Mathematics for Computer Scientists III (for statistics).

Type

Basic Lecture (6 ECTS) for BSc DSAI, CySec, and Computer Science; Advanced Lecture (6 ECTS) for all others except for the M.Sc. Cybersecurity.

Prof. Dr. Isabel Valera and Dr. Kavya Gupta

Thursdays, 16–18 o'clock in person in E.2.2 Lecture Hall 0.01 (Günter Hotz Hörsaal).  

5 assignment sheets, including theoretical and programming exercises. 

Tutorials 

All tutorials will be in person. 

Monday : 10-12h, 12-14h, 14-16h
Tuesday: 12-14h
Wednesday: 10-12h
Friday : 12-14h 

Office Hours

Prof. Dr. Isabel Valera and Dr. Kavya Gupta: after each lecture
Teaching Assistants: by appointment

Language

English

 Tentative Schedule of the Lectures.