Enrolment key: aml25
Credits: 6 ECTS (2SWS lecture + 2SWS exercise)
Modules:
- MSc FiMa: WP23 „Advanced Topics in Computer and Data Science B”
- MSc Math: WP42 „Überblick über ein aktuelles Forschungsgebiet B”
Description:
Real-world applications of machine learning require not only a strong theoretical foundation but also a solid knowledge of the methodologies, tools, and heuristics essential for implementing machine learning algorithms. However, the practical aspects of machine learning are often overlooked in mathematics programs. This course bridges that gap by providing students with hands-on experience in implementation and empirical analysis of machine learning algorithms — critical skills for those pursuing careers in data analysis or machine learning.
Content:
The course covers fundamental topics such as linear regression, gradient descent, regularization techniques, logistic regression, support vector machines (SVMs), and basic neural networks. Additionally, the course will explore advanced optimization methods, multi-class classification strategies, and ensemble learning techniques such as boosting and bagging.
A key component of the course is extensive programming in Python, using libraries such as NumPy, Matplotlib, Pandas, and scikit-learn. We will work with real datasets, including MNIST handwritten digits, the Boston Housing dataset, Wine dataset, etc.
