Hello, I want to record what I have been learning in my perosnal time. I didn't do good job on making and keeping the note, but I will be doing a better job onward. This list's purpose is to keep tabs of things, and let you know what I like more or less.
I signed up for the Coursera Plus in 2020 for 1 year. I have learned much.
- Specialization Self-Driving Cars, from University of Toronto
- Introduction to Self-Driving Cars
- State Estimation and Localization for Self-Driving Cars
- Visual Perception for Self-Driving Cars
- Motion Planning for Self-Driving Cars
- Specialization Accelerated Computer Science Fundamentals, from UIUC
- Object-Oriented Data Structures in C++
- Ordered Data Structures
- Unordered Data Structures
- Digital Signal Processing 1: Basic Concepts and Algorithms, from EPFL
- Digital Signal Processing 2: Filtering
- Fundamentals of Reinforcement Learning, from University of Alberta
- Introduction to Portfolio Construction and Analysis with Python, from EDHEC business school
- Version Control with Git, from Atlassian
- Agile with Atlassian Jira
- Pratical Time Series Analysis, from SUNY
- Financial Engineering and Risk Management Part 1, from Columbia
- Managing the Organziation, from UIUC
- Designing the Organization from UIUC
- Leading Teams: Building Effective Team Cultures, from UIUC
- Leadning Teams: Developing as a Leader, from UIUC
- Investment 1: Fundamentals of Performance Evaluation, from UIUC
- Learning How to Learn: Powerful mental tools to help you master tough subjects
See /LeetCode for code.
I recently started to give LeetCode a try, maybe learn a new language as well (July 2023)
- UMich-Curly Mobile Robotics
- Cyrill Stachniss, Photogrammetry (1, 2)
- Steve Brunton's playlist and book on Data-Driven Science and Engineering
- Link
- He keeps updating content for his channel. I finished his content up to 1st Edition of his book, DMD stuff.
- MIT's Intro to deep learning
- Fast ai's Practical Deep Learning, 2020 Link
- ...
- Introduction to Data-Centric AI
- UvA, Deep learning
- LLM Bootcamp
- The Mathematics of Gambling, Ed Thorp, book,
- Matrix Groups for Undergraduates, Kristopher Tapp, book,
- Harvard Applied Math 205
- Tubingen,
- Convex optimization
- Modern Robotics, Northwestern
- Dive into Deep Learning
- More manifold stuff, DL, ML, graph DL, and geometric algebra
- random matrix theory, and representation theory seem cool too
- Sports Performance Analytics, Coursera, U Michigan
- Investment Management with Python and Machine Learning, Coursera, EDHEC
- Intellectual Property Law, Coursera, UPenn
- Corporate & Commerical Law 1: Contracts & Employment Law, Coursera, UIUC
- Computational finance, the book with owls as cover
- Implied volatility