Prof. Emo Welzl and Prof. Bernd Gärtner
Bernd Gärtner (CAB G31.1);
David Steurer (CAB H37.1).
Tommaso D'Orsi (CAB H37.2);
Hung Hoang (CAB G19.2), contact assistant;
Saeed Ilchi (CAB G32.1);
Gleb Novikov (CAB H36.2);
Mon 15-16, ETF C1,
Tue 10-12, ETF C1.
|Credit Points:||8CP (261-5110-00L, 3V + 2U + 2A)|
This course teaches an overview of modern optimization methods, with applications in particular for machine learning and data science.
|Moodle:||All materials in the course are published through the moodle page of the course.|
|Prerequisites:||As background, we require material taught in the course "252-0209-00L Algorithms, Probability, and Computing". It is not necessary that participants have actually taken the course, but they should be prepared to catch up if necessary.|
|Grading:||There will be a written exam in the examination session. Furthermore, there will be two mandatory written special assignments during the semester. The final grade of the whole course will be calculated as a weighted average of the grades for the exam (80%) and the special assignments (20%).|
|Special Assignments:||At two times in the course of the semester, we will hand out specially marked exercises or term projects — the written part of the solutions are expected to be typeset in LaTeX or similar. Solutions will be graded, and the grades will account for 20% of the final grade. Assignments can be discussed with colleagues, but we expect an independent writeup.|
Date to be determined. The exam lasts 120 minutes, it is written and closed-book. No written material permitted!
The theoretical exercises are discussed in classes. Students are expected to try to solve the problems beforehand. Your assistant is happy to look at your solutions and correct/comment them. We will assign students to classes according to surnames. Attendance according to these assignments is not compulsory but encouraged. The details of the classes are as follows.
|Group||Time||Room||Students with Surnames (Last Names)||Assistant|
|A||Tue 13-15||HG D3.2||A - L||Yiming Yan|
|B||Tue 13-15||HG D5.2||M - Z||Saeed Ilchi|
These form a self-study component which provides guidance to implement some of the methods discussed in the lectures. Students are encouraged to attempt these exercises and check against the suggested solutions, which will be made available online some time after the release of the exercises. Although they are not discussed in the regular classes, students can contact the practical exercise assistant (Hung Hoang) with any questions.