Machine Learning
Introduction to Machine Learning
General Information
Lecturer: | Prof. Dr. Benno Stein |
Lab advisors: | Janek Bevendorff, Johannes Kiesel, Nailia Mirzakhmedova |
Workload: | 2 SWS Lecture, 1 SWS Lab |
ECTS Credits: | 4.5 or 6 depending on degree programme |
Lecture: | Th. 9:15am - 10:45am (weekly starting October 20th). Marienstraße 13 C - Hörsaal A |
Lab: | Th. 11:00am - 12:30pm (biweekly starting October 27th). Marienstraße 13 C - Hörsaal A |
Examination: | Tu. Feb 21st, 8:00am - 10:00am, Marienstraße 13 C - Hörsaal A + B |
Moodle: | moodle.uni-weimar.de/course/view.php?id=41421 |
Lecturenotes
Machine Learning > Introduction > Organization, Literature Machine Learning > Introduction > Learning Tasks Machine Learning > Introduction > Elements of Machine Learning Machine Learning > Introduction > Syntax and Models Overview Machine Learning > Machine Learning Basics > Concept Learning Machine Learning > Machine Learning Basics > From Regression to Classification Machine Learning > Machine Learning Basics > Evaluating Effectiveness Machine Learning > Linear Models > Logistic Regression Machine Learning > Linear Models > Overfitting and Regularization Machine Learning > Linear Models > Gradient Descent in Detail Machine Learning > Neural Networks > Perceptron Learning Machine Learning > Neural Networks > Multilayer Perceptron Machine Learning > Neural Networks > Advanced MLPs 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 > Bayesian Learning > Probability Basics Machine Learning > Bayesian Learning > Bayes Classification
Lab Class
Active participation in the lab class is a requirement for admission to the final exam.
To submit your solutions to the lab class exercises, you must enrol in the Moodle course for the lecture. Working in groups of up to 3 students is encouraged -- once you have formed a group, each member must select the same group number in Moodle.
- 27.10.2022 Lab Class 1
Lab class introduction
Group selection open [moodle] - 09.11.2022 23:59: submission deadline for exercise sheet 1 annotations [moodle]
- 10.11.2022 Lab Class 2
Lab class machine learning basics 1
Discussion of exercise sheet 1: [worksheet] - 24.11.2022 Lab Class 3
Lab class machine learning basics 2
Discussion of exercise sheet 2: [worksheet] - 08.12.2022 Lab Class 4
Lab class linear models
Discussion of exercise sheet 3: [worksheet] - 12.01.2023 Lab Class 5
Lab class neural networks
Discussion of exercise sheet 4: [worksheet] - 26.01.2023 Lab Class 6
Lab class decision trees
Discussion of exercise sheet 5: [worksheet] - 02.02.2023 Lab Class 7
Lab class bayesian learning + Q&A (both lecture and lab class time slot)
Discussion of exercise sheet 6: [worksheet]
Literature
Please find the literature in the lecture slides.