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 15th! 

Registration in CMS is open until October 31st, in LSF until February 7th.

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. Sara Magliacane

Thursdays, 16:00-18:00 in person in E.2.2 Lecture Hall 0.01 (Günter Hotz Hörsaal).   Lectures will be recorded and shared.

Tutorials

All tutorials will be in person in E1 3 HS001. They will start the week of October 29.

• Wednesday at 10:00–12:00
• Wednesday at 14:00–16:00
• Thursday at 10:00–12:00
• Thursday at 12:00–14:00
• Friday at 10:00–12:00
• Friday at 12:00–14:00

Note that the tutorial on Sunday, 16:00 is a dummy tutorial, intended for students that do not wish to attend any tutorials.

Midterms
& Exams

In order to access the exam, you will need to pass a midterm with multiple choice questions. This will not count for your final grade, but we will only consider it a pass/fail.

Midterm: Thursday, December 3, at 16:00-19:00

In case you fail the midterm, we will have also a re-exam for the midterm, so you can still qualify for the exam.

Re-exam for the midterm: TBD (most probably Saturday 16 January)

Main Exam - TBD (most probably 17 February)
Re-exam - TBD (most probably 23 March)

Office Hours

Prof. Dr. Sara Magliacane: before/after each lecture
Teaching Assistants: by appointment

Language

English

Tentative Schedule