Abu Mostafa’s Machine Learning MOOC – Now on EdX

This was in the pipeline for quite some time now. I have been waiting for his lectures on a platform such as EdX or Coursera, and the day has arrived. You can enroll and start with week 1’s lectures as they’re live now.

This course is taught by none other than Dr. Yaser S. Abu – Mostafa, whose textbook on machine learning, Learning from Data is #1 bestseller textbook (Amazon) in all categories of Computer Science. His online course has been offered earlier over here.


Dr. Abu-Mostafa received the Clauser Prize for the most original doctoral thesis at Caltech. He received the ASCIT Teaching Awards in 1986, 1989 and 1991, the GSC Teaching Awards in 1995 and 2002, and the Richard P. Feynman prize for excellence in teaching in 1996.

Live ‘One-take’ Recordings

The lectures have been recorded from a live broadcast (including Q&A, which will let you gauge the level of CalTech students taking this course). In fact, it almost seems as though Abu Mostafa takes a direct jab at Andrew Ng’s popular Coursera MOOC by stating the obvious on his course page.

A real Caltech course, not a watered-down version


Again, while enrolling note that this is what Abu Mostafa had to say about the online course:  “A Caltech course does not cater to short attention spans, and it may not provide instant gratification…[like] many MOOCs out there that are quite simple and have a ‘video game’ feel to them.” Unsurprisingly, many online students have dropped out in the past, but some of those students who “complained early on but decided to stick with the course had very flattering words to say at the end”.


  • Basic probability
  • Basic matrices
  • Basic calculus
  • Some programming language/platform (I choose Python!)

If you’re looking for a challenging machine learning course, this is probably one you must take.


Statistical Learning – 2016

On January 12, 2016, Stanford University professors Trevor Hastie and Rob Tibshirani will offer the 3rd iteration of Statistical Learning, a MOOC which first began in January 2014, and has become quite a popular course among data scientists. It is a great place to learn statistical learning (machine learning) methods using the R programming language. For a quick course on R, check this out – Introduction to R Programming

Slides and videos for Statistical Learning MOOC by Hastie and Tibshirani available separately here. Slides and video tutorials related to this book by Abass Al Sharif can be downloaded here.

The course covers the following book which is available for free as a PDF copy.

Logistics and Effort:


Rough Outline of Schedule (based on last year’s course offering):

Week 1: Introduction and Overview of Statistical Learning (Chapters 1-2)
Week 2: Linear Regression (Chapter 3)
Week 3: Classification (Chapter 4)
Week 4: Resampling Methods (Chapter 5)
Week 5: Linear Model Selection and Regularization (Chapter 6)
Week 6: Moving Beyond Linearity (Chapter 7)
Week 7: Tree-based Methods (Chapter 8)
Week 8: Support Vector Machines (Chapter 9)
Week 9: Unsupervised Learning (Chapter 10)

Prerequisites: First courses in statistics, linear algebra, and computing.


Statistics: The Sexiest Job of the Decade

Anyone who’s got a formal education in economics knows who Hal Varian is. He’s most popularly known for his book Intermediate Economics. He’s also the Chief Economist at Google. He is known to have famously stated more or less, that statisticians and data analysts would be the sexiest jobs of the next decade.

That has come true, to a great extent, and we’ll be seeing more.

Great places to learn more about data science and statistical learning:
1] Statistical Learning (Stanford)
2] The Analytics Edge (MIT)

In a paper called ‘Big Data: New Tricks for Econometrics‘, Varian goes on to say that:

In fact, my standard advice to graduate students these days is “go to the computer science department and take a class in machine learning.” There have been very fruitful collaborations between computer scientists and statisticians in the last decade or so, and I expect collaborations between computer scientists and econometricians will also be productive in the future.

See Also: Slides on Machine Learning and Econometrics