Supervised Learning.
In this course, we are going to dive deeper into one of the three major workhorses empowering the success of Machine Learning: Supervised Learning. Our goal is to build agents that can improve themselves and adapt their knowledge. But what is this knowledge we want them to pick up? How should they adapt themselves? How would we know whether their adaptation led to any improvement after all? Can we have confidence in their predictions? During this course, we are going to address all of these questions and many more.
You are looking for the latest DeepLearning architecture you can use straight off the bat to build your AI-app? I'm sorry, that's not going to happen during this course. We'll get the basics sorted out, so you can build whatever DeepLearning architecture you dream of. From scratch.
Jump on board and get your hands dirty with supervised learning.
...btw. Do you speak Python? Familiar with pandas, numpy, scipy, matplotlib, pytorch? No worries, we'll cover that, too.
Lecture:
- Wednesdays: 09:45 - 11:15 (A5.07)
Exercise:
- Wednesdays: 11:45 - 13:15 (B0.08a)
- Get a free educational license for the fabulous IDE PyCharm.
Certificate of achievement:
- written
- 90 min
- 5 ECTS
- at the end of this semester
- Trainer/in: Jurgovsky, Johannes