Course in Foundations of Machine Learning
|Lecturer||Jun.-Prof. Dr. Martin Potthast|
|Lab Advisors||Lukas Gienapp, Niklas Deckers|
|Workload||2 SWS Lecture, 1 SWS Lab|
|Lecture||Tuesday, 15:15 - 16:45, HS 1 (Hörsaalgebäude).|
|Lab||There are 4 groups (i.e. repetitions of the lab).
A and B: On selected Wednesdays, 11:15 - 12:45, Felix-Klein-Hörsaal (Paulinum, 5th floor).
C and D: On selected Tuesdays, 11:15 - 12:45, Felix-Klein-Hörsaal (Paulinum, 5th floor).
The specific dates for the 6 sessions can be found below: Organization.
|Exam||Written exam at the end of the semester.|
- The lecture will take place in a hybrid format. It will be streamed and recorded.
- In addition to the lecture, you will be provided with both theoretical and practical exercises. We will not collect or grade these exercises, but provide you with solutions (released at the same time). Nevertheless, you will still need to solve the exercises on your own/with a partner. You are responsible for your own learning success.
- We expect you to have studied the lecture, solved the exercises and reviewed the solutions before the corresponding lab session takes place. For that, you will also be able to make use of the videos that we will provide. The lab sessions will be done in a flipped classroom format. This only works if you are well prepared. The lab sessions will not be streamed or recorded.
The dates of your group's lab sessions can be found in the following table:
Topic Group a Group b Group c Group d 1 26.10.2022 19.10.2022 25.10.2022 18.10.2022 2 09.11.2022 02.11.2022 08.11.2022 01.11.2022 3 30.11.2022 23.11.2022 29.11.2022 22.11.2022 4 14.12.2022 07.12.2022 13.12.2022 06.12.2022 5 18.01.2023 11.01.2023 17.01.2023 10.01.2023 6 01.02.2023 25.01.2023 31.01.2023 24.01.2023
- Please also communicate with your fellow students to exchange about the course topics, the exercises and solution approaches.
- Lecture website - materials and announcements will be uploaded on this website.
- Email - important announcements will be sent out via email to students that are officially enrolled via AlmaWeb. Please notify Niklas Deckers immediately if you did not receive the welcome email at the start of the semester.
- Discord - you are invited to engage in discussions with your fellow students. We provide a Discord server that you are invited to join. The link will be provided in the welcome email.
The specific dates of the lab sessions can be found here: Organization.
Topic 1: Python and Maths Basics
- Material will be published on: 11.10.2022
- Relevant lectures: Until Introduction > Learning Tasks
Topic 2: Evaluation Using Numpy
- Material will be published on: 25.10.2022
- Relevant lectures: Until Machine Learning Basics > Evaluating Effectiveness
Topic 3: Linear Regression
- Material will be published on: 15.11.2022
- Relevant lectures: Until Linear Models > Overfitting & Regularization
Topic 4: Tree-Based Classification
- Material will be published on: 29.11.2022
- Relevant lectures: Until Decision Trees > Pruning
Topic 5: Bayesian Classification
- Material will be published on: 20.12.2022
- Relevant lectures: Until Bayesian Models > Bayesian Classification
Topic 6: Neural Networks
- Material will be published on: 17.01.2023
- Relevant lectures: Until Neural Networks > Multilayer Perceptron