Courses, lectures and resources that I plan to enjoy, am currently enjoying or have enjoyed.
To best way to learn is to put it into practice. So code it!
My choice is Python
The aim is to self-learn the equivalent of CMU's Master of Computer Vision
- Khan Academy: Linear Algebra
- 3Blue1Brown YT: Essence of Linear Algebra ❤️
- Edx Uni of Davidson: Applications of Linear Algebra (Part 2)
- Edx Uni of Texas: LAFF: Linear Algebra - Foundations to Frontiers
- MIT OCW: Linear Algebra by Prof Gilbert Strang ❤️
- MathTheBeautiful YT: Parts 3 & 4 Linear Algebra playlists by Prof Pavel Greenfield
- Khan Academy: Probability and Statistics
- Elements of Statistical Learning ebook
- MIT OCW: Probabilistic Systems Analysis and Applied Probability
- Coursera Uni of Washington: Computational Methods for Data Analysis
- DSP Guide ebook
- MIT OCW: Digital Signal Processing
- MathTheBeautiful YT: Partial Differential Equations
- Coursera Stanford Uni: Machine Learning
- Coursera Uni of Toronto: Neural Networks for Machine Learning
- Coursera Stanford Uni: Probabilistic Graphical Models
- Deep Learning Book
- Neural Networks and Deep Learning ebook
- Udacity Stanford Uni: Intro to Artifical Intelligence
- Edx UC Berkeley: Artifical Intelligence
- Edx Uni of Munich: Autonomous Navigation for Flying Robots
- Queensland Uni of Tech: Introduction to Robotics
- Multiple View Geometry in Computer Vision by Hartley & Zisserman (Best reference book for geometry in CV)
- EENG512 - Computer Vision by William Hoff (Matlab examples but videos are fantastic) 👍
- Standford Uni: CS231n: Convolutional Neural Networks for Visual Recognition
- CMU: 16-385 Computer Vision (Assignments in Matlab)
- Penn State Uni: CSE/EE486 Introduction to Computer Vision
- Udacity Georgia Tech: Introduction to Computer Vision
- Computer Vision: Algorithms and Applications ebook by Szeliski
- Computer Vision A Modern Approach by Forsyth, Ponce
- Princeton Vision & Robotics
To add: Kalman filters