course: Machine Learning: Supervised Methods
- teaching methods:
- lecture with tutorials
- responsible person:
- Prof. Dr. Tobias Glasmachers
- Prof. Dr. Tobias Glasmachers (Neuroinformatik)
- offered in:
- summer term
dates in summer term
- lecture with integrated tutorials Thursdays: from 10:00 to 14.00 o'clock in IA 0/158
This lecture will cover a contemporary spectrum of supervised learning methods. All lecture material will be in English.
The course will use the inverted classroom concept. Students work through the relevant lecture material at home. The material is then consolidated in a 4 hours/week practical session.
The field of machine learning constitutes a modern approach to artificial intelligence. It is situated in between computer science, neuroscience, statistics, and robotics, with applications ranging all over science and engineering, medicine, economics, etc. Machine learning algorithms automate the process of learning, thus allowing prediction and decision making machines to improve with experience.
The course requires basic mathematical tools from linear algebra, calculus, and probability theory. More advanced mathematical material will be introduced as needed. The practical sessions involve programming exercises in Python. Participants need basic programming experience. They are expected to bring their own devices (laptops).