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DSF > Teaching

Teaching Activities of the Institute

Autumn School 2026

Machine Learning I
  • Lecturer: Nihat Ay & Frank Röder
  • Period: September 14th, 2026 to October 2nd, 2026
  • Exam: October 2nd, 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

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
Recent Developments Towards a New Theory of Generalisation (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

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
TU Hamburg
Institute for
Data Science Foundations
Blohmstraße 15
21079 Hamburg
E-mail : dsf-office@tuhh.de
Phone : +49 40 306 01 4932
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