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Artificial Intelligence |
Overview
- OLAT
- Exercises in https://ki.inf.th-bingen.de/jupyter.
- Lecture recordings: Panopto
- Final exam at the end of the course
Exercises
- All exercises will be in the form of Jupyter notebooks and posted in OLAT
References
- "Deep Learning", Ian Goodfellow, Yoshua Bengio, Aaron Courville
- www.deeplearning.ai
History
| 1950s | first mentioning of "machine learning"; perceptron; neural networks |
| 1960s | nearest neighbor; backpropagation; discovery of fundamental limits for neural networks |
| 1970s | AI winter |
| 1980s | rediscovery of backpropagation; reinforcement learning |
| 1990s | AI winter; support vector machines; recurrent neural networks |
| 2000s | Netflix prize; ImageNet |
| 2010s | Deep Learning boom |
Machine translation
Google Translate
Object recognition
Protein folding
https://commons.wikimedia.org/wiki/File:C12orf29_AlphaFold.png
Games
But: deep learning is no magic pixie dust
https://commons.wikimedia.org/wiki/File:Fig-X_All_ML_as_a_subfield_of_AI.jpg
Why now?
What makes a successful DL teams?
1. Agressive data acquisition
2. Use every opportunity for automation
3. Excel at data warehousing
Module Goals
- Know relevant DL models and where to apply them
- Implement and train DL models
- Find out why they are not working
- Able to fix it
In the exercises we will implement a lot from scratch to see how the details work
Local Python / Jupyter installation:
Anaconda