2017-18 Open-Course Challenges

Inspired by Scott H.Young - MIT Challenges, I decide to design an Open-course Challenges of 2017-18 for myself. I hope that I could accomplish these goals in time.

Good luck!


  1. I am a year-1 PhD candidate in Information System. Thus the main goal of this challenge is to consolidate the academic foundation and broaden the knowledge. Instead of aim at obtaining a certificate (like on the MOOC), I try to selectively learn the materials which is beneficial for my research and study.
  2. As I’m interested in Machine Learning, Financial Engineering and FinTech, these learning materials focus on these areas.

General Information

Completed On Process
Completed On Process

Machine Learning

Machine Learning Cheat Sheet

Essential Cheat Sheets for deep learning and machine learning researchers: GitHub


Machine Learning

  1. Stanford CS229 - Machine Learning by Andrew Ng: Undergraduate, 1h15min/Lec, 20 Lectures, Course Page, Videos-1, Videos-2 19/20
  2. UCambridge - Course on Information Theory, Pattern Recognition, and Neural Networks by David MacKay: Undergraduate, 1h30min/Lec, 16 Lectures, Course Page, Videos-1, Videos-2 3/16
  3. Coursera - Neural Networks for Machine Learning by Geoffrey Hinton: Undergraduate, 16 Lectures, Course Page 1, Course Page 2, Videos 1/16
  4. Coursera - Probabilistic Graphical Models by Daphne Koller: 15 Lectures, Course Page 1 - Representation, Course Page 2 - Inference, Course Page 3 - Learning, Videos, Textbook, PGM@MIT, Spring 2013 1/15
  5. Udacity|Georgia Tech CS7646 - Machine Learning for Trading by Tucker Balch: Undergraduate, 28 parts, Course Page, Videos 1/28
  6. Coursera - Neural Networks and Deep Learning by Andrew Ng: Undergraduate, 46 parts, Course Page,Videos
  7. Stanford CS231N - Convolutional Neural Networks for Visual Recognition Spring 2017 by Li Feifei: Undergraduate, 1h15min/Lec, 16 Lectures, Course Page,Videos

Natural Language Processing

  1. Coursera - Natural Language Processing by Dan Jurafsky & Chris Manning: 23 Lectures, Course Page, Videos, Resources 37/102
  2. Stanford CS224N - Deep Learning for Natural Language Processing by Chris Manning: Undergraduate, 1h15min/Lec, 18 Lectures, Course Page, Videos 2/18
  3. Columbia COMS W4705 - Natural Language Processing by Michael Collins: Course Page, Videos

Artificial Intelligence

  1. Berkeley CS188 Introduction to Artificial Intelligence by Pieter Abbeel,Spring 2014: 25 Lectures, Course Page, Videos 1/25
  2. MIT 6.034 Artificial Intelligence by Patrick H. Winston, Fall 2010: 23 Lectures, Course Page, Videos 1/23
  3. Udacity|Intro to Artificial Intelligence: Course Page, Videos
  4. MIT 6.803/6.833 The Human Intelligence Enterprise: Course Page 2002,Course Page 2006,Course Page 2017
  1. Stanford CS224U - Natural Language Understanding by Bill MacCartney: For Graduate, Course Page
  2. Stanford CS224M : Multi Agent Systems by Yoav Shoham: For Graduate, Course Page, Videos
  3. Harvard CS281: Advanced Machine Learning Fall 2013: For Graduate, Course Page
  4. Columbia 4772 Advanced Machine Learning: For Graduate, Course Page
  5. MIT 6.864 Advanced Natural Language Processing: For Graduate, Course Page Fall 2010, Course Page Fall 2012, Course Page Fall 2016


  1. CMU 10-701/15-781 - Machine Learning by Tom Mitchell, Spring 2011: Undergraduate, 1h15min/Lec, 26 Lectures, Course Page, Videos
  2. CMU 10-702/36-702 - Statistical Machine Learning by Larry Wasserman, Spring 2016: Undergraduate, 1h15min, 24 Lectures, Course Page, Videos
  3. 国立台湾大学 - 機器學習基石 (Machine Learning Foundations) by 林軒田: Undergraduate, 65 parts, Course Page, Videos
  4. 国立台湾大学 - 機器學習技法 (Machine Learning Techniques) by 林軒田: Undergraduate, 65 parts, Videos
  5. CMU 10-701 - Introduction to Machine Learning by Alexander J. Smola, 2015: Undergraduate, 31 parts, Course Page, Videos
  6. Harvard CS109 - Data Science: Undergraduate, 1h15min/Lec, 20 Lectures, Course Page, Videos
  7. 龙星计划 清华大学-机器学习 by 余凯 张潼: 50min/Lec, 19 Lectures, Videos
  8. Willamette University CS449 - Neural Networks: Course Page, No Videos found
  9. Coursera: Data Science 专项课程: 10 Programs, Course Page
  10. MLSS 2013 Tübingen: Graphical Models by Christopher Bishop: Videos
  11. UBC CPSC540 Machine Learning by Nando de Freitas: For Graduate, 21 Lectures, Course Page, Videos
  12. Machine Learning by @mathematicalmonk on YouTube (Jeff Miller on Duke): 160 videos, 909k+ views, Videos


Introduction to Artificial Intelligence

  • Artificial Intelligence: A Modern Approach by S. Ruseull, P. Norvig: Amazon, E-Book

Machine Learning

Information Retrieval

  • Introduction to Information Retrieval by C. Manning, P. Raghavan, H. Schütze: Amazon, Home Page

Deep Learning:

  • Deep Learning by I. Goodfellow, Y. Bengio, A. Courville: Amazon,Home Page

Recommender System

Natural Language Processing

  • 统计自然语言处理(第2版)by 宗成庆: 豆瓣读书



Tools for Crawler

List of list

Notes, Blog, Talks and so on





Computer Science

Data Scientist Track


Data structure and algorithms

  • MIT 6.006 - Introduction to Algorithms, Fall 2011: 50min/Lec, 47 Lectures, Course Page, Videos 3/47
  • MIT 6.046J - Design and Analysis of Algorithms, Spring 2015: For Undergraduate, Course Page, Videos
  • MIT 18.409 - Algorithmic Aspects of Machine Learning Spring 2015 MIT: For Graduate, Course Page, Videos

Statistics & Probability

Probability Cheatsheet:

Statistics Cheatsheet:



  • MIT 18.650 - Statistics for Applications, Fall 2016: For Undergraduate / Graduate, 1h15min/Lec, 21 Lectures, Course Page, Videos 1/21
  • MIT 6.041SC - Probabilistic Systems Analysis and Applied Probability, Fall 2010: For Undergraduate / Graduate, Course Page, Videos
  • Stanford Online-StatLearning Statistical Learning by Trevor Hastie and Rob Tibshirani: 10 Lectures, Course Page, Notes&Excercise 1/10
  • MIT 6.041 / 6.431 Probabilistic Systems Analysis and Applied Probability, Fall 2010 by John Tsitsiklis: Course Page, Videos


  • Charles University in Prague NMFM404 Selected Software Tools for Finance and Insurance by Michal Pešta: Course Page


  • An Introduction to Statistical Learning: with Applications in R by G. James, D. Witten, T. Hastie, R. Tibshirani: Amazon, Home Page
  • The Elements of Statistical Learning by T. Hastie, R. Tibshirani, J. Friedman: Amazon, E-Book
  • 统计学习 by 李航: Amazon
  • Introduction to Probability and Statistics Using R by G. Jay Kerns: Amazon, E-Book


Math Tree


Financial Mathematics

  • Coursera - Financial Engineering and Risk Management: 51 parts, Course Page, Videos 0
  • MIT 18.S096 - Topics in Mathematics with Applications in Finance: 1h20min/Lec, 26 Lectures, Course Page, Videos


  • Stanford EE364A: Convex Optimization I: 1h15min/Lec, 19 Lectures, Course Page
  • Stanford EE364B: Convex Optimization II: 1h15min/Lec, 18 Lectures, Course Page

Stochastic Analysis

  • MIT EECS 6.262 - Discrete Stochastic Processes, Spring 2011: 1h20min/Lec, 25 Lectures, Course Page, Videos

Linear Algebra & Vector Calculus

  • MIT 18.02 Multivariatble Calculus, Fall 2007, by Dennis Auroux: For Undergraduate, Course Page, Videos
  • MIT 18.06 Linear Algebra by Gilbert Strang: For Undergraduate, Course Page, Videos

Finance & Economics

Financial Theories

Investment Desicion Making Problem

  • MIT 15.401 - Finance Theory I by Andrew Lo, Fall 2008: 1h10min/Lec, 20 Lectures, Course Page, Videos
  • MIT 15.402 - Finance Theory II by Dirk Jenter & Katharina Lewellen, Spring 2003: Course Page
  • Economic421 - Econometrics by Mark Thoma: 1h20min/Lec, 19 Lectures, Course Page, Videos
  • Information Economics: Videos
  • Yale Econ159 - Game Theory by Ben Polak: Course Page, Videos

Information System


  • Data mining/Text mining
  • Knowledge management
  • Social media/Social computing/Social network
  • Decision Support System
  • Network optimization
  • Stochastic Control Theory
  • Operation Research


  • Github
  • Hexo
  • Markdown
  • LatTex
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