This course is aimed at students who have no experience with Machine Learning and would like to understand what it is about and be able to apply Neural Network in their research.
At the end of the course, you should be able to apply Deep Learning to your own research based on worked-out examples.
Topics include:
1. Convolutional Neural Networks (CNN)
2. RNNs+Graphs
3. Attention Models and Transformers
4. Generative Models and Adversarial Training
5. AutoEncoders Anomaly Detection.
6. Reinforcement Learning
Each lecture will be divided into two parts:
1. Some overview of the subject
2. Hand-on training
This is not a theoretical course in Machine Learning, it is more like a hands-on course taught by users of machine learning.
There is no point in taking the course if you do not aim to exercise.
Last but not least, the course assumes some basic familiarity with coding in Python. It is advised to acquire some basic experience with Python. For example, you can follow https://www.kaggle.com/learn/python.
The course will be taught by Eilam Gross with extended teaching assistance by Etienne Dreyer, Nilotpal Kakati and Dmitrii Kobylianskii