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
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 of Machine Learning, it is more like a hands-on course taught by users of machine learning.
The course is given in the spirit of the Stanford CS23n1 Course but lectures might differ:
http://cs231n.stanford.edu/
2019 Course Web Page can be found here:
https://www.weizmann.ac.il/particle/Gross/ml-course
There is no point in taking the course if you do not aim to exercise. To that end, you are asked to open a cloud account BEFORE coming to the first lecture.
There are a few possibilities.
For the first lecture, you can open a google cloud account (recommended) or use Collab https://course.fast.ai/start_colab.html . To open a Google Cloud account please follow the instructions here: https://course.fast.ai/start_gcp.html.
Note, when opening a cloud account with Google you get a credit of $300 which is at least 3 times more than the cost of the usage needed for the course.
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 Sanmay Ganguly, and Jonathan Shlomi.