DSF: Teaching

Teaching Activities of the Institute

Winter term 2026/27

Dates and rooms of our courses in the winter term 2026/27. Binding information is in TUNE and Stud.IP.

Machine Learning II

Period: 21 Oct 2026 to 28 Jan 2027

Weekly schedule
Session	Day	Time	Room	Dates
Lecture	Thursday	11:30–13:00	Building D, Room 2.022	12
Exercise group 1	Wednesday	09:45–11:15	Building H, Room 0.01	13
Exercise group 2	Wednesday	11:30–13:00	Building H, Room 0.01	13

No sessions from 18 Dec 2026 to 5 Jan 2027. Also cancelled: Lecture on Thu, 12 Nov 2026.

Show all 38 dates

All dates, Machine Learning II, winter term 2026/27
Date	Time	Session	Room
Wed, 21 Oct 2026	09:45–11:15	Exercise group 1	Building H, Room 0.01
Wed, 21 Oct 2026	11:30–13:00	Exercise group 2	Building H, Room 0.01
Thu, 22 Oct 2026	11:30–13:00	Lecture	Building D, Room 2.022
Wed, 28 Oct 2026	09:45–11:15	Exercise group 1	Building H, Room 0.01
Wed, 28 Oct 2026	11:30–13:00	Exercise group 2	Building H, Room 0.01
Thu, 29 Oct 2026	11:30–13:00	Lecture	Building D, Room 2.022
Wed, 4 Nov 2026	09:45–11:15	Exercise group 1	Building H, Room 0.01
Wed, 4 Nov 2026	11:30–13:00	Exercise group 2	Building H, Room 0.01
Thu, 5 Nov 2026	11:30–13:00	Lecture	Building D, Room 2.022
Wed, 11 Nov 2026	09:45–11:15	Exercise group 1	Building H, Room 0.01
Wed, 11 Nov 2026	11:30–13:00	Exercise group 2	Building H, Room 0.01
Wed, 18 Nov 2026	09:45–11:15	Exercise group 1	Building H, Room 0.01
Wed, 18 Nov 2026	11:30–13:00	Exercise group 2	Building H, Room 0.01
Thu, 19 Nov 2026	11:30–13:00	Lecture	Building D, Room 2.022
Wed, 25 Nov 2026	09:45–11:15	Exercise group 1	Building H, Room 0.01
Wed, 25 Nov 2026	11:30–13:00	Exercise group 2	Building H, Room 0.01
Thu, 26 Nov 2026	11:30–13:00	Lecture	Building D, Room 2.022
Wed, 2 Dec 2026	09:45–11:15	Exercise group 1	Building H, Room 0.01
Wed, 2 Dec 2026	11:30–13:00	Exercise group 2	Building H, Room 0.01
Thu, 3 Dec 2026	11:30–13:00	Lecture	Building D, Room 2.022
Wed, 9 Dec 2026	09:45–11:15	Exercise group 1	Building H, Room 0.01
Wed, 9 Dec 2026	11:30–13:00	Exercise group 2	Building H, Room 0.01
Thu, 10 Dec 2026	11:30–13:00	Lecture	Building D, Room 2.022
Wed, 16 Dec 2026	09:45–11:15	Exercise group 1	Building H, Room 0.01
Wed, 16 Dec 2026	11:30–13:00	Exercise group 2	Building H, Room 0.01
Thu, 17 Dec 2026	11:30–13:00	Lecture	Building D, Room 2.022
Wed, 6 Jan 2027	09:45–11:15	Exercise group 1	Building H, Room 0.01
Wed, 6 Jan 2027	11:30–13:00	Exercise group 2	Building H, Room 0.01
Thu, 7 Jan 2027	11:30–13:00	Lecture	Building D, Room 2.022
Wed, 13 Jan 2027	09:45–11:15	Exercise group 1	Building H, Room 0.01
Wed, 13 Jan 2027	11:30–13:00	Exercise group 2	Building H, Room 0.01
Thu, 14 Jan 2027	11:30–13:00	Lecture	Building D, Room 2.022
Wed, 20 Jan 2027	09:45–11:15	Exercise group 1	Building H, Room 0.01
Wed, 20 Jan 2027	11:30–13:00	Exercise group 2	Building H, Room 0.01
Thu, 21 Jan 2027	11:30–13:00	Lecture	Building D, Room 2.022
Wed, 27 Jan 2027	09:45–11:15	Exercise group 1	Building H, Room 0.01
Wed, 27 Jan 2027	11:30–13:00	Exercise group 2	Building H, Room 0.01
Thu, 28 Jan 2027	11:30–13:00	Lecture	Building D, Room 2.022

Mathematics for Machine Learning

Period: 21 Oct 2026 to 28 Jan 2027

Weekly schedule
Session	Day	Time	Room	Dates
Lecture	Wednesday	09:45–11:15	Building A, Room 0.13	13
Exercise group 1	Wednesday	11:30–13:00	Building A, Room 0.14	13
Exercise group 2	Thursday	09:45–11:15	Building H, Room 0.03	12

No sessions from 18 Dec 2026 to 5 Jan 2027. Also cancelled: Exercise group 2 on Thu, 12 Nov 2026.

Show all 38 dates

All dates, Mathematics for Machine Learning, winter term 2026/27
Date	Time	Session	Room
Wed, 21 Oct 2026	09:45–11:15	Lecture	Building A, Room 0.13
Wed, 21 Oct 2026	11:30–13:00	Exercise group 1	Building A, Room 0.14
Thu, 22 Oct 2026	09:45–11:15	Exercise group 2	Building H, Room 0.03
Wed, 28 Oct 2026	09:45–11:15	Lecture	Building A, Room 0.13
Wed, 28 Oct 2026	11:30–13:00	Exercise group 1	Building A, Room 0.14
Thu, 29 Oct 2026	09:45–11:15	Exercise group 2	Building H, Room 0.03
Wed, 4 Nov 2026	09:45–11:15	Lecture	Building A, Room 0.13
Wed, 4 Nov 2026	11:30–13:00	Exercise group 1	Building A, Room 0.14
Thu, 5 Nov 2026	09:45–11:15	Exercise group 2	Building H, Room 0.03
Wed, 11 Nov 2026	09:45–11:15	Lecture	Building A, Room 0.13
Wed, 11 Nov 2026	11:30–13:00	Exercise group 1	Building A, Room 0.14
Wed, 18 Nov 2026	09:45–11:15	Lecture	Building A, Room 0.13
Wed, 18 Nov 2026	11:30–13:00	Exercise group 1	Building A, Room 0.14
Thu, 19 Nov 2026	09:45–11:15	Exercise group 2	Building H, Room 0.03
Wed, 25 Nov 2026	09:45–11:15	Lecture	Building A, Room 0.13
Wed, 25 Nov 2026	11:30–13:00	Exercise group 1	Building A, Room 0.14
Thu, 26 Nov 2026	09:45–11:15	Exercise group 2	Building H, Room 0.03
Wed, 2 Dec 2026	09:45–11:15	Lecture	Building A, Room 0.13
Wed, 2 Dec 2026	11:30–13:00	Exercise group 1	Building A, Room 0.14
Thu, 3 Dec 2026	09:45–11:15	Exercise group 2	Building H, Room 0.03
Wed, 9 Dec 2026	09:45–11:15	Lecture	Building A, Room 0.13
Wed, 9 Dec 2026	11:30–13:00	Exercise group 1	Building A, Room 0.14
Thu, 10 Dec 2026	09:45–11:15	Exercise group 2	Building H, Room 0.03
Wed, 16 Dec 2026	09:45–11:15	Lecture	Building A, Room 0.13
Wed, 16 Dec 2026	11:30–13:00	Exercise group 1	Building A, Room 0.14
Thu, 17 Dec 2026	09:45–11:15	Exercise group 2	Building H, Room 0.03
Wed, 6 Jan 2027	09:45–11:15	Lecture	Building A, Room 0.13
Wed, 6 Jan 2027	11:30–13:00	Exercise group 1	Building A, Room 0.14
Thu, 7 Jan 2027	09:45–11:15	Exercise group 2	Building H, Room 0.03
Wed, 13 Jan 2027	09:45–11:15	Lecture	Building A, Room 0.13
Wed, 13 Jan 2027	11:30–13:00	Exercise group 1	Building A, Room 0.14
Thu, 14 Jan 2027	09:45–11:15	Exercise group 2	Building H, Room 0.03
Wed, 20 Jan 2027	09:45–11:15	Lecture	Building A, Room 0.13
Wed, 20 Jan 2027	11:30–13:00	Exercise group 1	Building A, Room 0.14
Thu, 21 Jan 2027	09:45–11:15	Exercise group 2	Building H, Room 0.03
Wed, 27 Jan 2027	09:45–11:15	Lecture	Building A, Room 0.13
Wed, 27 Jan 2027	11:30–13:00	Exercise group 1	Building A, Room 0.14
Thu, 28 Jan 2027	09:45–11:15	Exercise group 2	Building H, Room 0.03

Autumn School 2026

Machine Learning I

Lecturer: Nihat Ay & Frank Röder
Period: September 14^th, 2026 to October 2^nd, 2026
Exam: October 2^nd, 2026, building D, Room 2.022
Legend:
- L = Lecture, building M, Room 0.526
- T = Theory exercise group, building M, Room 0.526/1.582
- P = Practical exercise group, building M, Room 0.526/1.582

Time	Mon 14th Sep	Tue 15th Sep	Wed 16th Sep	Thu 17th Sep	Fri 18th Sep	Mon 21st Sep	Tue 22nd Sep	Wed 23rd Sep	Thu 24th Sep	Fri 25th Sep	Mon 28th Sep	Tue 29th Sep	Wed 30th Sep	Thu 1st Oct	Fri 2nd Oct
09:00 - 10:30	L1	L3	self-study	L5	self-study	L7	self-study	L9	self-study	L11	self-study	L13	self-study	self-study
10:45 - 12:15	L2	L4		L6		L8		L10		L12		L14			exam
12:15 - 13:15	break	break		break		break		break		break		break
13:15 - 14:45	self-study	T1/P1		T2/P2		T3/P3		T4/P4		T5/P5		T6/P6
15:00 - 16:30	self-study	P1/T1		P2/T2		P3/T3		P4/T4		P5/T5		P6/T6

Summer term 2026

Machine Learning I

Lecture: Nihat Ay
Exercise: Manfred Eppe & Jesse van Oostrum & Frank Röder
Lecture: Wednesday: 09:45 - 11:15, Building H, Audimax I
Exercise:
- Monday: 09:45 - 11:15, Building D, Room 1.024
- Monday: 11:30 - 13:00, Building D, Room 1.024
- Tuesday: 09:45 - 11:15, Building D, Room 1.023
- Tuesday: 11:30 - 13:00, Building D, Room 1.023

Information Geometry

Lecture: Nihat Ay
Exercise: Pradeep Kumar Banerjee & Adwait Datar
Lecture: Friday: 09:45 - 11:15, Building I, Audimax II
Exercise:
- Tuesday: 14:00 - 15:30, Building A, Room 2.34
- Wednesday: 11:30 - 13:00, Building N, Room 0.009

Deep Reinforcement Learning

Organiser: Pradeep Kumar Banerjee & Frank Röder
Tuesday, 11:00 - 12:30
Location: HipOne, Room 5.002

Causal Machine Learning

Organiser: Jesse van Oostrum & Leon Sierau

Wednesday,14:30 - 16:00

Location: HipOne, Room 5.002

Winter term 2025/2026

Machine Learning II

Lecture: Adwait Datar & Frank Röder
Exercise: Adwait Datar & Frank Röder
Lecture: Thursday: 09:45 - 11:15, Building H, Room 0.16
Exercise:
- Wednesday: 09:45 - 11:15, Building H, Room 0.01
- Wednesday: 11:30 - 13:00, Building H, Room 0.01
- Thursday: 11:30 - 13:00, Building H, Room 0.03

Reinforcement Learning

Lecture: Manfred Eppe & Frank Röder
Exercise: Manfred Eppe & Frank Röder
Lecture: Monday: 09:45 - 11:15, Building HS28, Room 1.007
Exercise: Monday: 11:30 - 13:00, Building HS28, Room 1.007

Winter term 2024/2025

Machine Learning II

Lecture: Nihat Ay & Manfred Eppe
Exercise: Nihat Ay & Manfred Eppe & Adwait Datar
Lecture: Wednesday: 09:45 - 11:15, Building K, Room 0506
Exercise:
- Tuesday: 09:45 - 11:15, Building H, Room 0.007
- Thursday: 11:30 - 13:00, Building H, Room 0.02

Das Kino-Seminar: Können Maschinen Mensch sein?

Organisers: Nihat Ay & Pradeep Kumar Banerjee
Tuesday, 12:30 - 14:00
Location: Blohmstraße 15 (HIP One), 5th Floor, Room 5.002

Causal Inference in Machine Learning

Organisers: Nihat Ay & Carlotta Langer
Wednesday, 12:30 - 14:00
Location: Building D, Room D-1.023

Large Language Models and Information Theory: Decoding Words and Bits

Organiser: Pradeep Kumar Banerjee
Monday, 14:00 - 15:30
Location: Blohmstraße 15 (HIP One), 5th Floor, Room 5.002

Summer term 2024

Machine Learning I

Lecture: Nihat Ay
Exercise: Nihat Ay & Manfred Eppe & Adwait Datar
Lecture: Wednesdays, 09:45 – 11:15, Room K – 0506
Tutorials:
- Group 1: Monday, 09:45 – 11:15, Room N – 0.009
- Group 2: Monday, 11:30 – 13:00, Room N – 0.009
- Group 3: Tuesday, 09:45 – 11:15, Room D – 1.023
- Group 4: Tuesday, 11:30 – 13:00, Room D - 1.023
More Info: Stud.IP

Statistical Models

Lecture: Nihat Ay & Matthias Schulte
Exercise: Nihat Ay & Matthias Schulte
Lecture:
- Tuesday: 13:15 - 14:45, weekly, Building D, Room 2.022
- Thursday: 15:00 - 16:30, fortnightly, Building D, Room 2.022
Exercises: Thursday: 15:00 - 16:30, fortnightly, Building D, Room 2.022

Introduction to Reinforcement Learning

Organiser: Manfred Eppe
Wednesday, 11:30 – 13:00 (twice a month)
Location: Blohmstraße 15 (HIP One), 5th Floor, Room 5.002
More Info: PDF, Stud.IP

Introduction to Deep Learning

Organiser: Pradeep Banerjee
Tuesdays, 11:30 – 13:00
Location: Blohmstraße 15 (HIP One), 5th Floor, Room 5.002
More Info: Stud.IP

Deep Reinforcement Learning

Organiser: Pradeep Banerjee
Tuesdays, 14:00 - 15:30
Location: Blohmstraße 15 (HIP One), 5th Floor, Room 5.002
More Info: Stud.IP

Summer term 2023

Machine Learning I

Lecture: Nihat Ay
Exercise: Nihat Ay & Manfred Eppe & Adwait Datar
Lecture: Wednesdays, 09:45 – 11:15, Room K – 0506
Tutorials:
– Group 1: Wednesdays, 11:30 – 13:00, Room H – 0.01
– Group 2: Thursdays, 09:45 – 11:15, Room A – 0.01
– Group 3: Thursdays, 11:30 – 13:00, Room D – 0.011
More Info: Stud.IP

Mensch und Maschine – Herausforderungen durch Künstliche Intelligenz

Organiser: Nihat Ay
Thursdays, 15:30 – 16:30
Location: Blohmstraße 15 (HIP One), 5th Floor, Room 5.002
More Info: PDF, Stud.IP

Introduction to Reinforcement Learning

Organiser: Manfred Eppe
Tuesdays, 11:30 – 15:00
Location: Blohmstraße 15 (HIP One), 5th Floor, Room 5.002
More Info: PDF, Stud.IP

Winter term 2022/2023

Machine Learning II

Lecture: Nihat Ay
Exercise: Nihat Ay & Manfred Eppe
Lecture: Tuesdays, 15:00 – 16:30, Room K – 0506
Exercise:
– Calculation of the Exercises: Thursdays, 9:45 – 11:15, Room O – 0.007
– Mathematical Questions & Answers: Thursdays, 11:30 – 13:00, Room A – 1.16
More Info: Stud.IP

Das Kino-Seminar: Können Maschinen intelligent sein?

Organisers: Nihat Ay & Manfred Eppe
Wednesdays, 11:30 – 13:00
Location: Blohmstraße 15 (HIP One), 5th Floor, Room 5.002
More Info: PDF, Stud.IP

Introduction to Data Science

Organisers: Manfred Eppe, Tobias Knopp, Matthias Schulte, Stefan Schulte und Dominik Schallmoser
Fridays, 13:15 – 14:45
Location: D – 2.022
more Info:PDF, Stud.IP

Summer term 2022

Machine Learning I

Nihat Ay, Manfred Eppe & Csongor Várady
lecture & exercise for Data Science (DSBS)
lecture: Wednesdays, 9:45 – 11:15, Room K – 0506
exercise:
– Group 1: Thursdays, 9:45 – 11:15, Room A – 0.01
– Group 2: Thursdays, 11:30 – 13:00, Room D – 0.011
– Group 3: Tuesday, 11:30 – 13:00, Room A – 0.14
electronic exam: September 14th, 2022
more Info: Stud.IP

Winter term 2021/2022

Generieren von Strukturen in Bildern – Die Gibbs-Sampling-Methode (student team project)

Organisers: Nihat Ay & Csongor Várady
Student team project for Informatik-Ingenieurwesen (IIW)
more info: PDF
Timetable: PDF

Das Kino-Seminar: Können Maschinen intelligent sein?

Organiser: Nihat Ay
Thursdays, 11:30 – 13:00
Location: Blohmstraße 15 (HIP One), 5. Stock, Raum 5.002
more info: PDF, Stud.IP
Timetable: PDF

Summer term 2021

Gradientenmethoden in der Lerntheorie (Seminar)

Organiser: Nihat Ay
Masterstudierende ECTS-Punkte: 3

The following courses and seminars took place at the Max Planck Institute for Mathematics in the Sciences and the Leipzig University.

Winter term 2020/2021

Kernel Methods in Learning Theory

Nihat Ay
Location: video broadcast
Abstract, registration and more info

Recent Developments Towards a New Theory of Generalisation (Seminar)

Nihat Ay (prepared in collaboration with Juan Pablo Vigneaux)
Location: video broadcast
Abstract, registration and more info, Outline of the Seminar

Summer term 2019

Artificial Neural Networks and Machine Learning: Theoretical Foundations II

Nihat Ay
Thursdays, 11:15 – 12:45
Location: MPI MiS, A3 02
more info

Winter term 2018/2019

Artificial Neural Networks and Machine Learning: Theoretical Foundations I

Nihat Ay
more info

Summer term 2017

Information Theory II

Nihat Ay
Tuesdays, 11:00 – 12:30
Location: MPI MiS, A3 02

Winter term 2016/2017

Information Theory I

Nihat Ay
Tuesdays, 11:00 – 12:30
Location: MPI MiS, A3 02

Summer term 2016

Grundlagen der Robotik und Seminar Morphological Computation

Keyan Ghazi-Zahedi
Tuesdays, 13:00 – 15:00
Location: University Leipzig, SG 2-14

Summer term 2015

Reinforcement Learning – An Introduction

Keyan Ghazi-Zahedi
Thursdays, 10:15 – 11:45
Location: A2, MPI MiS

Winter term 2014/2015

Geometric Aspects of Graphical Models and Neural Networks

Nihat Ay, Guido Montufar
Wednesdays, 10:15 – 11:45; first on November 26th
Location: A2, MPI MiS

Summer term 2013

Information Theory II

Nihat Ay
Wednesdays, 11:30 – 13:00; first on April 17th
Location: A2, MPI MiS

Winter term 2012/2013

Information Theory

Nihat Ay
Wednesdays, 11:00 – 12:30; first on October 10th
Location: A2, MPI MiS

Winter term 2011/2012

Second part of the IMPRS Ringvorlesung “Higher Dimensions”

Nihat Ay
Date: 9.,23.,30. November and 7., 14. December

Winter term 2010/2011

Stochastische Prozesse

Nihat Ay
Thursdays, 15:15 – 16:45, Hs 15

Optimierung und Komplexität

Nihat Ay

Summer term 2010

Stochastic Differential Equations

Nihat Ay
It will take place on Wednesdays, starting April 14, at 10:15 in A1.

Summer term 2009

Mathematical Learning Theory and Neural Networks

Nihat Ay

Summer term 2007

Concepts of Causality in Biology and Medicine (Seminar)

Korbinian Strimmer & Nihat Ay

Quantum Mechanics: basic mathematical structures and their operational interpretation

Arleta Szkola

Winter term 2006/2007

Information Theory II

Arleta Szkola

Random Graphs – New Developments Beyond the Erdös-Renyi Model

Tyll Krüger

Summer term 2006

Information Theory I

Arleta Szkola

Graphical Models and Causality

Nihat Ay

Startsemester
- 06.10.2026
- 06.10.2026
Endsemester
- 06.10.2026
- 06.10.2026
Gruppiert nach

Teaching Activities of the Institute

Winter term 2026/27

Machine Learning II

Mathematics for Machine Learning

Autumn School 2026

Machine Learning I

Summer term 2026

Machine Learning I

Information Geometry

Deep Reinforcement Learning

Causal Machine Learning

Winter term 2025/2026

Machine Learning II

Reinforcement Learning

Winter term 2024/2025

Machine Learning II

Das Kino-Seminar: Können Maschinen Mensch sein?

Causal Inference in Machine Learning

Large Language Models and Information Theory: Decoding Words and Bits

Summer term 2024

Machine Learning I

Statistical Models

Introduction to Reinforcement Learning

Introduction to Deep Learning

Deep Reinforcement Learning

Summer term 2023

Machine Learning I

Mensch und Maschine – Herausforderungen durch Künstliche Intelligenz

Introduction to Reinforcement Learning

Winter term 2022/2023

Machine Learning II

Das Kino-Seminar: Können Maschinen intelligent sein?

Introduction to Data Science

Summer term 2022

Machine Learning I

Winter term 2021/2022

Generieren von Strukturen in Bildern – Die Gibbs-Sampling-Methode (student team project)

Das Kino-Seminar: Können Maschinen intelligent sein?

Summer term 2021

Gradientenmethoden in der Lerntheorie (Seminar)

Winter term 2020/2021

Kernel Methods in Learning Theory

Recent Developments Towards a New Theory of Generalisation (Seminar)

Summer term 2019

Artificial Neural Networks and Machine Learning: Theoretical Foundations II

Winter term 2018/2019

Artificial Neural Networks and Machine Learning: Theoretical Foundations I

Summer term 2017

Information Theory II

Winter term 2016/2017

Information Theory I

Summer term 2016

Grundlagen der Robotik und Seminar Morphological Computation

Summer term 2015

Reinforcement Learning – An Introduction

Winter term 2014/2015

Geometric Aspects of Graphical Models and Neural Networks

Summer term 2013

Information Theory II

Winter term 2012/2013

Information Theory

Winter term 2011/2012

Second part of the IMPRS Ringvorlesung “Higher Dimensions”

Winter term 2010/2011

Stochastische Prozesse

Optimierung und Komplexität

Summer term 2010

Stochastic Differential Equations

Summer term 2009

Mathematical Learning Theory and Neural Networks

Summer term 2007

Concepts of Causality in Biology and Medicine (Seminar)

Quantum Mechanics: basic mathematical structures and their operational interpretation

Winter term 2006/2007

Information Theory II

Random Graphs – New Developments Beyond the Erdös-Renyi Model

Summer term 2006

Information Theory I

Graphical Models and Causality

TU Hamburg