Education
Our Courses
- Geometry of Computer Vision and Computer Graphics (CTU in Prague)
- Advanced Robot Kinematics (CTU in Prague)
- Geometry of Computer Vision (Charles University in Prague)
- Geometric Problems in Robotics (Charles University in Prague)
- Geometry of Computer Vision (Ukrainian Catholic Univerity in Lviv)
Our Talks and Tutorials
- Solving Minimal Problems in the Age of Machine Learning @ GRASP Lab UPENN 2022
- The Art of Solving Minimal Problem Tutorial @ CVPR 2019
- Minimal Problems in Computer Vision Talk @ ICERM 2018
- Algebraic Geometry & 3D Reconstruction Talk @ ISPRS 2016
- Solving Minimal Problems in Computer Vision Tutorial @ ICCV 2015
- Omnidirectional Vision Course Tutorial @ ICCV 2003
- Stereo Geometries of Non-central Cameras Talk 2001
Mathematics
Lectures
- E Nicholson Anyone from Anywhere Can Learn Anything
- Linear Algebra, Groups, Rings, Fields, Number theory
- Sets, Logic, Probability, Statistics
- Metric spaces, Topology, Real analysis, Complex analysis
- Numerical Analysis, Differential Equations
- R E Borcherds 196884
- Complex analysis
- Groups, Lie groups, Representation theory, Theory of Numbers, Modular forms
- Algebraic geometry, Algebraic topology
- D Chan‘s DanielChanMaths
- R Donley MathDoctorBob
- J Strom, K Astrom, T Akenine-Moller Linear Algebra in Immersive Math
- T W Judson Abstract Algebra Online
Popularization
- Numberphile
- Socratica
- B Polster Mathologer
- G Sanderson 3Blue1Brown
- Teddy Rocks Maths
Computer Vision
- CTU Geometry of Computer Vision and Computer Graphics
- C Daniilidis, J Shi. Robotics: Perception Coursera Online Course
Robotics
- Steve Brunton (LA, SVD, Dynamics, Control, Data science, Koopman)
- CTU Advanced Manipulator Kinematics
- CUNI NMMB442: Geometric Problems in Robotics
- Northwestern University Coursera Course Modern Robotics, Course 1: Foundations of Robot Motion
- Northwestern University Coursera Course Modern Robotics, Course 2: Robot Kinematics
Machine Learning
Courses
- Matt Yeldin. Intro to Aspects of Macine Learning (youtube) – A crasch course on ML
- A Seff. What is Automatic Differentiation? Talk on youtube 2020 explains [Baydin 2018]
- J Pfrommer. Optimization Methods for Machine Learning and Engineering (youtube) 2021
- fast.ai courses (Part 1: Practical Deep Learning for Coders + Part 2: Deep Learning from the Foundations)
- NYU Deep Learning by Yann LeCun & Alfredo Canziani
- MIT 6.S191: Introduction to Deep Learning
- Stanford CS230 Deep Learning
- Stanford CS231n Convolutional Neural Networks for Visual Recognition
- UC Berkeley CS 285 Deep Reinforcement Learning [S Levine Deep Robotic Learning]
- CMU 11-785 Introduction to Deep Learning
- D Silver’s Course on Reinforcement Learning @ UCL
Talks & Lectures
- L Fridman’s Artificial Intelligence Podcast [L Fridman 2020 Machine Learning – The State of the Art]
- L Fridman’s AI, Deep & RL Learning
- A Karpathy NN in TESLA Autopilot in 2019 (42:00-01:50)
- Distill Journal – “Machine Learning Research Should Be Clear, Dynamic and Vivid”
- G Sanderson 3Blue1Brown Neural Networks [NN, GD, BP1, BP2]
- M Nielsen Neural Networks and Deep Learning 2015 [github]
Study, research, science
- How to write a good CV paper Talk 2017
- M Nielsen Three myths about scientific peer review
- Nielsen’s Blog – AI, Physics, Science and More
- Gowers’s Weblog – Math and More
- T Tao’s What’s New
- Numberphile, P vs NP, Godel’s IT, …