Research Group Matthias Kaschube - Teaching

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Theoretical Neuroscience I

Winter Semester 2020/2021
Instructor: Matthias Kaschube

This course is an online course. You can enrol here:

moodle

Machine Learning I

Winter Semester 2020/2021
Instructor: Matthias Kaschube

This course is an online course. You can enrol here:

moodle

Seminar Computational Neuroscience

Winter Semester 2020/2021
Instructor: Matthias Kaschube

This course is an online course. You can enrol here:

moodle

Previous Semesters

Machine Learning II

Summer Semester 2020
Instructor: Matthias Kaschube

This course is an online course. You can enrol here:

moodle

Seminar Computational Neuroscience

Summer Semester 2020
Instructor: Matthias Kaschube

This course is an online course. To enrol please send an email with your name, matriculation number and study programme to Bastian Eppler. Please include [comp_neuro] in the subject.

For further information, please use the following link:

moodle

Seminar Pattern Analysis and Machine Intelligence

Summer Semester 2020
Instructor: Matthias Kaschube

This course is an online course. Further information will be provided soon.

The date for the first meeting will be communicated via email and the homepage.

Computational Neuroscience Seminar

Winter Semester 2019/2020
Instructor: Matthias Kaschube

Time: Thursday 10:00-11:30,

Room: Robert Mayer Straße 11, SR 11

QIS/LSF Entry

Papers
Brain Map

Machine Learning I

Winter Semester 2019/2020
Instructors: Nils Bertschinger and Matthias Kaschube

Link to course homepage

Pattern Analysis and Machine Inteligence

Winter Semester 2019/2020
Instructors: Nils Bertschinger, Matthias Kaschube, Visvanathan Ramesh

Link to course homepage

Theoretical Neuroscience I

Winter Semester 2019/2020
Instructor: Matthias Kaschube

To enroll in this course please send an email with your name, matriculation number and [TN1] in the subject to Bastian Eppler.

Lecture: 8:30 am

Exercises: 10:15 am

Room: Robert-Mayer-Straße 11, SR 307

Description

This course gives a general introduction to the field of Computational or Theoretical Neuroscience. We largely follow the text book “Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems” by P. Dayan and L.F. Abbott.

Course Contents

Date Lecture Problem Set Tutorial
2019-10-18 Week #1 [slides]
[organisation]
 - -
2019-10-25 Week #2 [Slides]
 - Tutorial 1 [Slides]
2019-11-01 Week #3 [Slides]
 Problem Set #1 [pdf]
 Tutorial 2 [Slides]
2019-11-08 Week #4 [Slides]
 Problem Set #2 [pdf]
 -
2019-11-15 Week #5 [Slides]
 Problem Set #3 [pdf]
 -
2019-11-22 Week #6 [Slides]
 Problem Set #4 [pdf]
 -
2019-11-29 Week #7 [Slides]
 Problem Set #5 [pdf]
 Solution Problem Set #3 [pdf]
2019-12-06 Week #8 [Slides]
 Peoblem Set #6 [pdf]
 -
2019-12-13 Week #9 [Slides]
 Problem Set #7 [pdf] [material]
 -
2019-12-20 Week #10 [Slides]
 - -
2020-01-17 Week #11 [Slides]
 Problem Set #8 [pdf]
 -
2020-01-24 Week #12 [Slides]
 Problem Set #9 [pdf]
 -
2020-01-31 Week #13 [Slides]
 Problem Set #10 [pdf]
 -
2020-02-07 Week #14 [Slides]
 - Solution Problem Set #9 [pdf]

Machine Learning II

Summer Semester 2018
Instructors: Nils Bertschinger, Matthias Kaschube

Lecture: 16:15-17:45, Neue Mensa - NM 113

Tutorial: 14:15 - 15:45, Neue Mensa - NM 114

QiS/LSF Entry

lecture material

Theoretical Neuroscience II

Summer Semester 2018

Instructor: Matthias Kaschube

Lecture: Friday 10:00–12:00

Exercises: Friday 08:30–10:00

Description

Advanced topics in theoretical neuroscience, building on the course TN - Theoretical Neuroscience. Topics include computation in neural systems, dynamical properties of neural networks, neural coding, unsupervised learning, models of development.

Course Contents

Date Lecture Problem Set Tutorial
2018-04-13 -
 - -
2018-04-20 Week #2 [slides]
 - -
2018-04-27 Week #3 [slides]
 - -
2018-05-04 Week #4 [slides]
 Set #1 [pdf]
 Differential Equations [slides]
2018-05-11 Week #5 [slides]
 - Fourier Transform [slides]
2018-05-18 Week #6 [slides]
 Set #2 [pdf]
 -
2018-05-25 Week #7 [slides]
 Set #3 [pdf]
 -
2018-06-01 Week #8 [slides]
 -
 -
2018-06-08 Week #9 [slides]
 - -
2018-06-15 Week #10 [slides]
 Set #4 [pdf]
 -
2018-06-22 Week #11 [slides] Set #5 [pdf]
 -

Machine Learning I

Wintersemester 2017/2018
Instructors: Matthias Kaschube, Nils Bertschinger

Lecture: 16:15-17:45, Magnus Hörsaal

Tutorial: 14:15 - 15:45, SR 11

Exam:   20th February, 14:00-16:00, Magnus Hörsaal   (90 min)

13th March, 14:00-16:00, Magnus Hörsaal   (90 min)

Timline

Date Lecture Problem Set Tutorial
2017-10-17

week1.pdf

- -
2017-10-24 week2.pdf Exercise 1 Linear Algebra
2017-10-31 Reformation Day Holiday
2017-11-07 week3.pdf Exercise 2 Exercises
2017-11-14 week4.pdf - -
2017-11-21 week5.pdf Exercise 3 How-To: Posterior Distribution
2017-11-28 week6.pdf Exercise 4 How-To: Linear Model
2017-12-05 week7.pdf Exercise 5
2017-12-11 week8.pdf Exercise 6
2017-12-19 week9.pdf Exercise 7 Training Data
2018-01-09 week10.pdf - -
2018-01-16 week11.pdf Exercise 8
2018-01-23 week12.pdf Minimal Solution for Exercise 7
2018-01-30 week13.pdf Tensor Flow Introduction

Theoretical Neuroscience I

Winter Semester 2017/2018

Instructors: Matthias Kaschube, Jochen Triesch

Lecture: Friday 10:15–11:45, FIAS 0.200 Campus Riedberg

Exercise: Friday 12:15–13:45, FIAS 0.200 Campus Riedberg

 

Description

This course gives a general introduction to the field of Computational or Theoretical Neuroscience. We largely follow the text book “Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems” by P. Dayan and L.F. Abbott.

 

Course Contents

Date Lecture Problem Set Tutorial
2017-10-20

Week #1

- -
2017-10-27 Week #2 Set #1 DGLs/FourierTransform
2017-11-03 Week #3 Set #2 -
2017-11-10 Week #4 Set #3 -
2017-11-17 Week #5 Set #4 -
2017-11-24 - Set #5 -
2016-12-01 Week #6 Set #6 neuron.mat experiment.m
2016-12-08 Week #7 - -
2016-12-15 - - -
2016-12-22 Week #8 Set #7 -
2017-12-29 - - -
2018-01-05 - - -
2018-01-12 Week #9 Set #8 -
2018-01-19 Week #10 Set #9 -
2018-01-26 Week #11 Set #10 -
2018-02-02 Week #12 Set #11 -
2018-02-09 Week #13 - -