AI in-depth material
- I've created this repository to contain multiple courses taken on- & off- campus. The courses vary ranging across different fields, mainly focusing on Artificial Intelligence in a broader sense. The idea is to provide a concise and complete reference.
- I mainly included code and slides, however sometimes I add my personal notes about the topics. Related research papers are also included. The aim is to provide an ease access for full course material. The contents belong to original authors, only answer code and notes are mine.
- This is an overview for the coursework as listed in this repository. They are arranged by topics.
Hyperlinks are for certificates if applicable. - More MOOC from TUM can be found here
i. Informatics
1. Aritifical Intelligence
a. Fundamentals of Artificial Intelligence (IN2062) - TUM
b. Deep Learning Specialization - deeplearning.ai
c. TensorFlow Developer - deeplearning.ai
d. Introduction to Deep Learning (IN2346) - TUM
e. Advanced Introduction to Deep Learning - HSE
f. Bayesian Methods for Machine Learning - HSE
g. Machine Learning (IN2064) - TUM
h. Practical Reinforcement Learning - HSE
2. Web Development
- Python3 Programming Specialization - UMich
- Django For Everybody Specialization - UMich
- DevOps on AWS - AWS
ii. Finance - Quantitative
1. ML & RL in Finance Specialization - NYU
- [x] Guided Tour of Machine Learning in Finance
- [x] Fundamentals of Machine Learning
- [ ] Reinforcement Learning in Finance
- [ ] Overview of Advanced Methods of RL in Finance
2. Blockchain Specialization - UBafflo
iii. Preliminaries
1. Mathematics for Machine Learning Specialization - ICL
2. Basic Mathematical Methods for Imaging and Visualization - TUM
3. Aerial Robotics - UPenn
4. Computer Vision Basics - UBafflo
iv. Uncategorized
1. From Big Bang to Dark Energy - Tokyo University