Course in Foundations of Machine Learning
General Information
| Lecturer | Jun.-Prof. Dr. Martin Potthast |
| Lab Advisors | Christopher Akiki, Janos Borst, Erik Körner, Christopher Schröder |
| Workload | 2 SWS Lecture, 1 SWS Tutorial |
| Lecture | Thursdays, 9:15 - 10:45 |
| Lab | Thursdays, 11:15 - 12:45 |
| Contact | Moodle forum, or via Email |
| Exam | All information related to the exam has been published on Moodle. In case you are still not in the Moodle course, please immediately contact us via Email. |
| Moodle | Foundations of Machine Learning |
Announcements
- Please check Moodle for details about the exam.
Lecturenotes
Machine Learning > Introduction > Organization, Literature Machine Learning > Introduction > Learning Problems Machine Learning > Machine Learning Basics > Regression Machine Learning > Machine Learning Basics > Concept Learning Machine Learning > Machine Learning Basics > Evaluating Effectiveness Machine Learning > Linear Models > Logistic Regression Machine Learning > Linear Models > Regularization Machine Learning > Neural Networks > Perceptron Learning Machine Learning > Neural Networks > Gradient Descent Machine Learning > Neural Networks > Multilayer Perceptron Machine Learning > Decision Trees > Decision Trees Basics Machine Learning > Decision Trees > Impurity Functions Machine Learning > Decision Trees > Decision Tree Algorithms Machine Learning > Decision Trees > Decision Tree Pruning Machine Learning > Statistical Learning > Probability Basics Machine Learning > Statistical Learning > Bayes Classification
Lab Class
- 05.11.2020
Lab class introduction; Exercise sheet 1: Intro, Basics [worksheet]
- 12.11.2020
Tutorial.
Python basics. [notebook]
Jupyter. [notebook]
- 16.11.2020 23:59: submission deadline for exercise sheet 1
- 19.11.2020
Discussion of exercise sheet 1
Exercise sheet 2: Concept Learning [worksheet]
- 26.11.2020
Tutorial: scientific Python. [notebook]
- 02.12.2020 23:59: submission deadline for exercise sheet 2
Last chance to change group selections in Moodle
- 03.12.2020
Discussion of exercise sheet 2
Exercise sheet 3: Linear Models [worksheet] [notebook] [training-data] [bonus-challenge-testing-data]
Regularized basis expansion [notebook]
- 18.12.2020 23:59: submission deadline for exercise sheet 3
Bonus challenge [leaderboard]
- 07.01.2021
Implementing the multilayer perceptron [notebook]
- 13.01.2021
Exercise sheet 4: Neural Networks / Decision Trees [worksheet] [training-data] [bonus-challenge-testing-data]
- 14.01.2021
Discussion of exercise sheet 3
- 27.01.2021 23:59: submission deadline for exercise sheet 4
Bonus challenge [leaderboard]
- 28.01.2021
Discussion of exercise sheet 4
Exercise sheet 5 [worksheet] [training-data] [bonus-challenge-testing-data]
- 11.02.2021
Discussion of exercise sheet 5
Bonus challenge [leaderboard]