December, 2012


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Dec 12

My Education in Machine Learning via Coursera, A Review So Far

As of today I’ve completed my fifth course at Coursera, all but one being directly related to Machine Learning. The fact that you can now take classes given by many of most well known researchers in their field who work at some of the most lauded institutions for no cost at all is a testament to the ever growing impact that the internet has on our lives.  It’s quite a gift that these classes started to become available at right about the same time as when Machine Learning demand started to sky rocket (and at right about the same time that I entered the field professionally).

Note that all effort estimations include the time spent watching lectures, reading related materials, taking quizes and completing programming assignments.  Classes are listed in the order they were taken.

Machine Learning (Fall 2011)
Estimated Effort: 10-20 Hours a Week
Taught by Andrew Ng of Stanford University, this class gives a whirlwind tour of the traditional machine learning landscape. In taking this class you’ll build basic models for Regression, Neural Networks, Support Vector Machines, Clustering, Recommendation Systems, and Anomaly Detection. While this class doesn’t cover any one of these topics in depth, this is a great class to take if you want to get your bearings and learn a few useful tricks along the way. I highly recommend this class for anyone interested in Machine Learning who is looking for a good place to start.

Probabilistic Graphical Models (Spring 2012)
Estimated Effort: 20-30 Hours a Week
Taught by Daphne Koller of Stanford University, who de facto wrote The Book on Probabilistic Graphical Models (weighing in at 1280 small print pages). This class was a huge time investment, but well worth the effort. Probabilistic Graphical Models are the relational databases of the Machine Learning world in that they provide a structured way to represent, understand and infer statistical models. While Daphne couldn’t cover the entire book in a single class, she made an amazing effort of it. After you complete this course you will most definitely be able to leverage many different kinds of Probabilistic Graphical Models in the real world.

Functional Programming Principles in Scala (Fall 2012)
Estimated Effort: 3-5 Hours a Week
Taught by Martin Odersky who is the primary author of the Scala programming language. I entered this class with an existing strong knowledge of functional programming and so I’d expect this class to be a bigger time investment for someone who isn’t quite as comfortable with the topic. While not directly related to Machine Learning, knowledge of Scala allows leverage of distributed platforms such as Hadoop and Spark which can be quite useful in large scale Entity Resolution and Machine Learning efforts. Also related, one of the most advanced frameworks for Probabilistic Graphical Models is written in and designed to be used from Scala.  In taking this class you’ll most certainly become proficient in the Scala language, but not quite familiar with the full breadth of its libraries. As far as functional programming goes, you can expect to learn quite a bit about the basics such as recursion and immutable data structures, but nothing so advanced as co-recursion, continuation passing or meta programming. Most interesting to myself was the focus on beta reduction rules for the various language constructs.  These together loosely form a primer for implementing functional languages.

Social Network Analysis (Fall 2012)
Estimated Effort: 5-10 Hours a Week
Taught by Lada Adamic of the University of Michigan. Social Network Analysis stands out from the others as I’ve never been exposed anything quite like it before. In this class you learn to measure various properties of networks and several different methods for generating them. The purpose in this is to better understand the structure, growth and spread of information in real human social networks. The focus of the class was largely on intuition and I was a bit unhappy with the sparsity of the mathematics, but this certainly makes it a very accessible introduction to the topic. After completing this class I guarantee you’ll see new insight into your corporate structure and will see your twitter network in a whole new way. If I were to pick one class from this list to recommend to a friend no matter background or interest, this would be it.

Neural Networks for Machine Learning (Fall 2012)
Estimated Effort: 10-30 Hours a Week
Taught by Geoffrey Hinton of the University of Toronto, who is a pioneer and one of the most well respected people in his field. Note that going in you’ll be expected to have a strong working knowledge of Calculus, which is not a prerequisite for any of the other classes listed here. I had hoped that this class would have been as worthwhile as the Probabilistic Graphical Models course given its instructor, but sadly it was not. Regretfully, I can only say that this class was poorly put together. It has a meager four programming assignments, the first two of which follow a simple multiple choice coding formula, and the following two of which are unexpectedly much more difficult in requiring you to both derive your own equations and implement the result of that derivation. It was extremely hard to predict how much time I would need to spend on any given assignment.  Having already learned to use Perceptrons and simple Backpropagation in Andrew Ng’s class, the only new hands-on skill I gained was implementing Restricted Boltzmann Machines. To be fair, I did acquire quite a bit of knowledge about the Neural Networks landscape, and Restricted Boltzmann Machines are a core component of Deep Belief Networks.  However, looking back at the sheer quantity of skills and knowledge I gained in the other classes listed here, I can’t help but feel this class could have been much better.