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.

Teaching

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”.

Prerequisites

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.

Through this post, I’m sharing Python code implementing the median of medians algorithm, an algorithm that resembles quickselect, differing only in the way in which the pivot is chosen, i.e, deterministically, instead of at random.

Its best case complexity is O(n) and worst case complexity O(nlog_{2}n)

I don’t have a formal education in CS, and came across this algorithm while going through Tim Roughgarden’s Coursera MOOC on the design and analysis of algorithms. Check out my implementation in Python.

I get the following output:

51
100 loops, best of 3: 2.38 ms per loop

Note that on the same input, quickselect is faster, giving us:

Deep learning became a hot topic in machine learning in the last 3-4 years (see inset below) and recently, Google released TensorFlow (a Python based deep learning toolkit) as an open source project to bring deep learning to everyone.￼

If you have wanted to get your hands dirty with TensorFlow or needed more direction with that, here’s some good news – Google is offering an open MOOC on deep learning methods using TensorFlow here. This course has been developed with Vincent Vanhoucke, Principal Scientist at Google, and technical lead in the Google Brain team. However, this is an intermediate to advanced level course and assumes you have taken a first course in machine learning, or that you are at least familiar with supervised learning methods.

Google’s overall goal in designing this course is to provide the machine learning enthusiast a rapid and direct path to solving real and interesting problems with deep learning techniques.

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.

MIT’s Fall 2015 iteration of 6.00.2xstarts today. After an enriching learning experience with 6.00.1x, I have great expectations from this course. As the course website mildly puts it, 6.00.2x is an introduction to using computation to understand real-world phenomena. MIT OpenCourseware (OCW) mirroring the material covered in 6.00.1x and 6.00.2x can be found here.

The course follows this book by John Guttag (who happens to be one of the instructors for this course). However, purchasing the book isn’t a necessity for this course.

One thing I loved about 6.00.1x was its dedicated Facebook group, which gave a community / classroom-peergroup feel to the course. 6.00.2x also has a Facebook group. Here’s a sneak peak:

Thesyllabus and schedule for this course is shown below. The course is spread out over 2 months which includes 7 weeks of lectures.

The prerequisites for this course are pretty much covered in this set of tutorial videos that have been created by one of the TAs for 6.00.1x. If you’ve not taken 6.00.1x in the past, you can go through these videos (running time < 1hr) to judge whether or not to go ahead with 6.00.2x.

I have finally embarked on my first machine learning MOOC / Specialization. I love Python, and this course uses Python as the language of choice. Also, the instructors assert that Python is widely used in industry, and is becoming the de facto language for data science in industry. They use IPython Notebook in their assignments and videos.

The specialization offered by the University of Washington consists of 5 courses and a capstone project spread across about 8 months (September through April). The specialization’s first iteration kicked off yesterday.

Key Learning Outcomes
– Identify potential applications of machine learning in practice.
– Describe the core differences in analyses enabled by regression, classification, and clustering.
– Select the appropriate machine learning task for a potential application.
– Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.
– Represent your data as features to serve as input to machine learning models.
– Assess the model quality in terms of relevant error metrics for each task.
– Utilize a dataset to fit a model to analyze new data.
– Build an end-to-end application that uses machine learning at its core.
– Implement these techniques in Python.

Week-by-Week Week 1: Introductory welcome videos and the instructors’ views on the future of intelligent applications Week 2: Predicting House Prices (Regression) Week 3: Classification (Sentiment Analysis) Week 4: Clustering and Similarity: Retrieving Documents Week 5: Recommending Products Week 6: Deep Learning: Searching for Images

EDIT

It’s been 3 days since the course began, and here’s how the classmate demographic looks like:

I enrolled in Introduction to Computer Science and Programming Using Python with the primary objective of learning to code using Python. This course, as the name suggests, is more than just about Python. It uses Python as a tool to teach computational thinking and serves as an introduction to computer science. The fact that it is a course offered by MIT, makes it special.

As a matter of fact, this course is aimed at students with little or no prior programming experience who feel the need to understand computational approaches to problem solving. Eric Grimson is an excellent teacher (also Chancellor of MIT) and he delves into the subject matter to a surprising amount of detail.

The video lectures are based on select chapters from an excellent book by John Guttag. While the book isn’t mandatory for the course (the video lectures do a great job of explaining the material on their own), I benefited greatly from reading the textbook. There are a couple of instances where the code isn’t presented properly in the slides (typos or indentation gone wrong when pasting code to the slides), but the correct code / study material can be found in the textbook. Also, for explanations that are more in-depth, the book comes in handy.

MIT offers this course in 2 parts via edX. While 6.00.1x is is an introduction to computer science as a tool to solve real-world analytical problems, 6.00.2x is an introduction to computation in data science. For a general look and feel of the course, this OCW link may be a good starting point. It contains material including video lectures and problem sets that are closely related to 6.00.1x and 6.00.2x.

Each week’s material of 6.00.1x consists of 2 topics, followed by a Problem Set. Problem Sets account for 40% of your grade. Video lectures are followed by finger exercises that can be attempted any number of times. Finger exercises account for 10% of your grade. The Quiz (kind of like a mid-term exam) and the Final Exam account for 25% each. The course is of 8 weeks duration and covers the following topics (along with corresponding readings from John Guttag’s textbook).

From the questions posted on forums, it was apparent that the section of this course that most people found challenging, was efficiency and orders of growth – and in particular, the Big-O asymptotic notation and problems on algorithmic complexity.

Lectures on Classes, Inheritance and Object Oriented Programming (OOP) were covered really well in over 100 minutes of video time. I enjoyed the problem set that followed, requiring the student to build an Internet news filter alerting the user when it noticed a news story that matched that user’s interests.

The final week had lectures on the concept of Trees, which were done hurriedly when compared to the depth of detail the instructor had earlier gone to, while explaining concepts from previous weeks. However, this material was covered quite well in Guttag’s textbook and the code for tree search algorithms was provided for perusal as part of the courseware.

At the end of the course, there were some interesting add-on videos to tickle the curiosity of the learner on the applications of computation in diverse fields such as medicine, robotics, databases and 3D graphics.

The Wiki tab for this course (in the edX platform) is laden with useful links to complement each week of lectures. I never got around to reading those, but I’m going through them now, and they’re quite interesting. It’s a section that nerds would love to skim through.

I learnt a great deal from this course (scored well too) putting in close to 6-hours-a-week of study. It is being offered again on August 26, 2015. In the mean time, I’m keeping my eyes open for MIT’s data science course (6.00.2x) which is likely to be offered in October, in continuation to 6.00.1x.