Online Math and Algorithms courses

Fundamental

• Learning How to Learn: Powerful mental tools to help you master tough subjects, University of California, San Diego, Coursera

• How to Learn Math: For Students, Spring 2014, Lagunita Stanford

• Effective Thinking Through Mathematics, UT Austin, edX

• Introduction to Mathematical Thinking, Stanford Coursera

• Precalculus, Arizona State University, edX

  • Course Videos

  • Reference Free Books

Calculus

• Single Variable Calculus, Fall 2010, MIT OCW

• Multivariable Calculus, Fall 2010, MIT OCW

Linear Algebra

• English

  • Linear Algebra, MIT OCW

  • Computational Linear Algebra, fastai

• Mandarin

  • Linear Algebra I, NCTU OCW

  • Linear Algebra II, NCTU OCW

Advanced Calculus

• Mandarin

  • Advanced Calculus I, NCTU OCW

  • Advanced Calculus II, NCTU OCW

  • Advanced Calculus I, NTHU OCW

  • Advanced Calculus II, NTHU OCW

Complex Analysis

• English

  • Introduction to Complex Analysis, Wesleyan Coursera

• Mandarin

  • Complex Analysis, NCTU OCW

Abstract Algebra

• Abstract Algebra, Harvard (Youtube)

• Abstract Algebra, Socratica, Youtube

• Introduction to Galois Theory, National Research University Higher School of Economics, Coursera

Real Analysis

• English

  • Real Analysis, Francis Su, Youtube

• Mandarin

  • Real Analysis I, NCTU OCW

  • Real Analysis II, NCTU OCW

Functional Analysis

• English

  • Functional Analysis, University of Nottingham

• Mandarin

  • Matrix Analysis, NCTU OCW

Probability

• Probabilistic Systems Analysis and Applied Probability, Fall 2013, MIT OCW

• Computational Probability and Inference, edX MIT

• Probabilistic Programming and Bayesian Methods for Hackers, Github

Statistics

• English

  • Statistics with R Specialization, Duke Coursera

  • Statistical Learning, Stanford

  • Statistics for Application, Fall 2016, MIT OCW

  • Statistical Rethinking with Python and PyMC3

• Mandarin

  • Undergraduate: Basic Statistics, NCTU OCW

  • Undergraduate: Advanced Statistics, NCTU OCW

  • Graduate: Statistics, NCTU OCW

  • Graduate: Advanced Statistics, NCTU OCW

Differential Equations

• English

  • Differential Equations, MIT OCW

• Mandarin

  • Differential Equations, NCTU OCW

Numerical Method

• Numerical Methods for CSE, ETH Zurich

Signal Processing

• English

  • Signal and System, MIT OCW

  • Digital Signal Processing, EPFL Coursera

  • Discrete-Time Signal Processing, MIT edX

  • Adaptive Signal Processing, 2013, IITM

  • Adaptive Signal Processing, nptel, Youtube

  • Statistical Signal Processing, RICE

  • Statistical Signal Processing, UCCS

  • Kalman and Bayesian Filters in Python, GitHub

• Mandarin

  • Signal and System, NTU OCW

  • Introduction to Digital Speech Processing, NTU OCW

Control System

• English

  • Introduction to Linear Dynamical Systems, Stanford

  • Electronic Feedback Systems, MIT OCW

• Mandarin

  • Nonlinear Control System, NCTU OCW

Queueing Theory

• Queueing Theory, Florida Atlantic University

Information Theory

• Information Theory, Coursera (The Chinese University of Hong Kong)

Communication

• English

  • Principles of Digital Communications I, Fall 2006, MIT OCW

  • Principles of Digital Communication II, Spring 2005, MIT OCW

• Mandarin

  • Introduction to Communication, NTHU OCW

Stochastic processes

• English

  • Stochastic Processes, National Research University Higher School of Economics, Coursera

  • Discrete Stochastic Processes, MIT OCW

• Mandarin

  • Stochastic Processes, NCTU OCW

Game Theory

• Game Theory, Stanford University, The University of British Columbia, Coursera

• Game Theory II: Advanced Applications, Stanford University, The University of British Columbia, Coursera

Convex Optimization

• Convex Optimization I, Stanford

• Convex Optimization II, Stanford

• Convex Optimization, Spring 2015, CMU

Discrete Mathematics

• Mathematics for Computer Science, Spring 2015, MIT OCW

• Great Theoretical Ideas in Computer Science, Spring 2015, CMU

Cryptography

• Cryptography I, Stanford, Coursera

• Cryptography, University of Maryland, Coursera

Bitcoin, Blockchain and Tangle

• Bitcoin and Cryptocurrency Technologies, Princeton University, Coursera

• Bitcoin and Cryptocurrencies, Autumn 2016, Stanford

• Blockchain Fundamentals DeCal, Spring 2018, Berkeley

• Blockchain Developers DeCal, Spring 2018, Berkeley

• IOTA Tutorials, Mobilefish.com, Youtube

Algorithms

1. Introduction to Algorithms, Fall 2011, MIT OCW

2. Design and Analysis of Algorithms, Spring 2015, MIT OCW

3. Algorithms, Part I, Princeton University, Coursera

4. Algorithms, Part II, Princeton University, Coursera

5. Analysis of Algorithms, Princeton Coursera

6. Analytic Combinatorics, Princeton Coursera

7. Automata Theory, Standford Lagunita

8. Advanced Algorithms, Spring 2016, MIT

9. Advanced Data Structures, Fall 2017, MIT

10. Geometric Folding Algorithms: Linkages, Origami, Polyhedra, Spring 2017, MIT

11. Algorithms for Planar Graphs and Beyond, Fall 2011, MIT

12. Algorithmic Lower Bounds: Fun with Hardness Proofs, Fall 2014, MIT

Financial Mathematics

• English

  • Topics in Mathematics with Applications in Finance, Fall 2013, MIT OCW

• Mandarin

  • Introduction to Financial Mathematics I, NCTU OCW

  • Introduction to Financial Mathematics II, NCTU OCW

Data Science

• Introduction to Computational Thinking and Data Science, MITx

• Foundations of Data Science, Berkeleyx

• CS109 Data Science, Harvard

• Practical Data Science, CMU

• Functional Programming in Scala Specialization, EPFL, Coursera

• Practical Probabilistic Programming, Manning

• Scala: Guide for Data Science Professionals, Packet

• Scala: Applied Machine Learning, Packet

Artificial Intelligence

• Intro to AI, Berkeley

• Artificial Intelligence, MIT OCW

• Microsoft Professional Program for Artificial Intelligence

Machine learning

• Facebook’s Field Guide to Machine Learning video series

• Machine Learning Crash Course, Google

• Machine Learning, Standford, Coursera

• Machine Learning for Natural Language, Harvard

• Machine Learning, CMU

• Advanced Introduction to Machine Learning, CMU

Probabilistic Graphical Models

• Probabilistic Graphical Models, Standford, Coursera

• Probabilistic Graphical Models, CMU

Neural Network, Deep Learning

• Neural Networks for Machine Learning, University of Toronto, Coursera

• Convolutional Neural Networks for Visual Recognition, Standford (video)

• Deep Learning for Self-Driving Cars, MIT

• Deep Learning for Natural Language Processing, Standford (video)

• Tensorflow for Deep Learning Research, Standford (video)

• Deep Learning Specialization, Coursera

• Introduction to Deep Learning, Spring 2018, CMU

Reinforcement Learning

• UCL Course on RL, London’s Global University

• Reinforcement Learning, Udacity

• Deep Reinforcement Learning, Fall 2017, UC Berkeley

Formal Verification / Formal Method

• Programming Languages, Autumn 2017, University of Washington

• So you want to be a proof engineer?

• Algorithmic Verification, UNSW

• Advanced Topics in Software Verification, UNSW

• Formal Reasoning About Programs, MIT

Miscellaneous

• Graph Theory, Sarada Herke, Youtube

• Basic Modeling for Discrete Optimization, The University of Melbourne, The Chinese University of Hong Kong, Coursera

• Advanced Modeling for Discrete Optimization, The University of Melbourne, The Chinese University of Hong Kong, Coursera

• Discrete Optimization, The University of Melbourne, Coursera

• Combinatorial Mathematics, tsinghuax, edX

• The Fourier Transform and its Applications, Standford