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Massachusetts Institute of Technology

Machine Learning with Python: from Linear Models to Deep Learning

Massachusetts Institute of Technology via edX

Overview

If you have specific questions about this course, please contact us atsds-mm@mit.edu.

Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk.

As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control.

In this course, students will learn about principles and algorithms for turning training data into effective automated predictions. We will cover:

  • Representation, over-fitting, regularization, generalization, VC dimension;
  • Clustering, classification, recommender problems, probabilistic modeling, reinforcement learning;
  • On-line algorithms, support vector machines, and neural networks/deep learning.

Students will implement and experiment with the algorithms in several Python projects designed for different practical applications.

This course is part of theMITx MicroMasters Program in Statistics and Data Science. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit https://micromasters.mit.edu/ds/.

Syllabus

Lectures :

  • Introduction
  • Linear classifiers, separability, perceptron algorithm
  • Maximum margin hyperplane, loss, regularization
  • Stochastic gradient descent, over-fitting, generalization
  • Linear regression
  • Recommender problems, collaborative filtering
  • Non-linear classification, kernels
  • Learning features, Neural networks
  • Deep learning, back propagation
  • Recurrent neural networks
  • Recurrent neural networks
  • Generalization, complexity, VC-dimension
  • Unsupervised learning: clustering
  • Generative models, mixtures
  • Mixtures and the EM algorithm
  • Learning to control: Reinforcement learning
  • Reinforcement learning continued
  • Applications: Natural Language Processing

Projects :

  • Automatic Review Analyzer
  • Digit Recognition with Neural Networks
  • Reinforcement Learning

Taught by

Regina Barzilay and Tommi Jaakkola

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Reviews

2.3 rating, based on 21 reviews

Start your review of Machine Learning with Python: from Linear Models to Deep Learning

  • Anonymous
    I like Professor Regina Barzilay's part and TAs are awesome. Professor Tommi Jaakkola - I am sure is a wonderful person and extraordinary researcher -but his teaching style makes me feel like I am the stupidest person in the planet. This course would have been lot better had they let TAs do all the lecturing. I thought every MIT professor were as good as Walter Lewin or Patrick Winston. I now know I was wrong, terribly wrong!

    If you are enrolled in this course, compare how they teach support vector machine in the terribly delivered course with how Professor Patrick Winson taught it. (https://www.youtube.com/watch?v=_PwhiWxHK8o).
  • Anonymous
    I agree with the other review that it is the worst MITx course ever. We usually watch MITx courses and feel inspired. After watching most MITx courses on youtube, I feel like most students fail because they don't get good education such as the one at MIT. However, this course is absolute disgrace to be called a MIT course.
  • Pra
    “Any fool can make something complicated. It takes a genius to make it simple.” - Woody Guthrie

    I hope the course team thinks through this quote before they repeat that it is a 'graduate level' course as a justification on why this course is poorly delivered.
  • Anonymous
    Worst MIT courses I ever took. Lectures are ineffective and boring. If this course was to be put on youtube, there will be significantly more dislikes than the likes, that is why they do not put it on youtube.
  • Anonymous
    I have finished several online courses the last 3 years. I can safely say that this is the worst online course I have ever seen. The lectures are very bad and short. There are almost no examples. I guess the logic of the course is to provide some short...
  • Anonymous
    This is an excellent course, well taught by both professors, with challenging problem sets and exams, and with interesting programming assignments in python. There are prerequisites in terms of probability, statistics, linear algebra and python coding, but this is intended to be taken as the final course in the micromasters so much is covered in previous courses, and the prerequisites are clearly stated. I enjoyed and learnt a lot from this course (it’s not a watered down, easy version but that it what makes it rewarding and worthwhile). If you have the right background and prerequisites, do not be put off by the previous reviewers!
  • Anonymous
    Do not recommend. Lectures are fairly thin. They often mention new terms without providing a definition. There is a ton of homework and most is on material not covered by lecture. There are no handouts/notes or textbook that accompany the course. You'll spend 12-18 hours per week, 10% on lectures, 90% on homework which is often poorly worded and confusing. The forums are full of desperate calls for help. Basically you'll be teaching yourself the material. If you stick through it, at the end you'll be good at deciphering cryptic math problems, not so much at applying machine learning concepts to the real world.
  • Anonymous
    I agree that if you don't have enough time or you are behind on some off the prerequisites that this course will confuse a lot of people, but what seems difficult in the beginning becomes easy in the end and I find that I understand some of the more complex ML books a lot better now. My score should be rather good, but I will go over everything again after the course is done, to have it really stick in my head.

    but if you want a course that just shows you the high-level logic and then the functions/packages, then this course is not for you.
  • Anonymous
    very bad teaching style, where to even start. Below I try to highlight few that stand out:

    1. No continuity between lectures and assignments

    2. No good examples covered in lectures

    3. Sentences very verbose, you will have to repeat the video multiple times to understand the message professor is trying to deliver.

    Overall, so many concepts are not covered or taught effectively that I felt I learnt only 10% of the course put together.
  • Anonymous
    I think the reviews below are very harsh. It's true that it is not as good as the Stanford one I followed on Coursera, but still the Python projects are interesting and well done. I feel that the videos are short on purpose, to give you the most important information. This leaves possibility for personal work if you want to dig further. Some people might be frustrated not to get more details directly in the package.
  • Anonymous
    Only a bot built by Prof. Jaakkola can give 5 star to this course. Most people who disliked this course was not because it was difficult but because it was horribly delivered. I signed up to this course for the rigor! If people were really put off by the rigor and depth of the course, people would have given bad ratings to other courses such as Probability. However, this is not the case.
  • Anonymous
    When I see the other reviews, I wonder if we did pursue the same course. This course is for the most part a complete work on machine learning and people are warned that this course will take up a significant amount of time (I do believe the course mentions...
  • Anonymous
    Definitely a very overloaded course. I do not recommend it at all, besides very expensive the certificate and super high probabilities of losing the investment
  • Anonymous
    This is a REAL course, for REAL students. This is not a MOOC where you fool yourself that you learned something. It is just a real university course that keeps away all the idiots.
  • Anonymous
    This course is not intended for people who are only interested in application of machine learning. Very difficult to understand. The lecture is not useful at all and the homework is very difficult which do not have any relationship with the content of lecture.
  • Anonymous
    This course is horrible, it sucks. I don't recommend it at all. Expensive, poorly explained content a many more unpleasant things.
  • Anonymous
    This course is crop. If you read this feedback don't waste your money on this course. Definitely one of the worst courses ever.
  • Anonymous
    Professor Tommi S. Jaakkola, please watch this: https://youtu.be/4a0FbQdH3dY?t=3443 (the url is at the specific timestamp so that you won't 'waste' your time). Seriously, if you wrote something ineligibly why wouldn't you redo the video?

    This was the first MOOC I paid for. I enrolled even though it did not have a review then because I was under the assumption that all MIT courses are as great as the ones by Walter Lewin or Eric Lander.

    Lesson learnt, never enroll in a course unless there are excellent reviews. You made a lot of us fool because there were no reviews, but you won't be able to tarnish the reputation of MIT lecturers that Lewin and Lander have built.
  • Anonymous
    This intellectually challenging course is a perfect introduction to machine learning. Lectures are split into concise videos with exercises in between to emphasize understanding.

    In each of five units, students implement the theory in a skeletal-code project. There are homework, a mid-term and a final exam. A course taught by fields experts, it was certainly worth my time.

    Note: be sure to meet the pre-requistes and consider the whole micromaster program!
  • Anonymous
    I am having a lot of fun with this course. It wants you to do some personal research to understand the concepts but that is actually all about machine learning. I am enjoying the guidance of mit team. As going through the lectures it can tastes bitter but you will soon know you are on the real learning path.

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