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California Institute of Technology

Learning From Data (Introductory Machine Learning)

California Institute of Technology via edX

This course may be unavailable.


This introductory computer science course in machine learning will cover basic theory, algorithms, and applications. Machine learning is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to automatically learn how to perform a desired task based on information extracted from the data. Machine learning has become one of the hottest fields of study today and the demand for jobs is only expected to increase. Gaining skills in this field will get you one step closer to becoming a data scientist or quantitative analyst.

This course balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures follow each other in a story-like fashion:

  • What is learning?
  • Can a machine learn?
  • How to do it?
  • How to do it well?
  • Take-home lessons.


The topics in the story line are covered by 18 lectures of about 60 minutes each plus Q&A.

  • Lecture 1: The Learning Problem
  • Lecture 2: Is Learning Feasible?
  • Lecture 3: The Linear Model I
  • Lecture 4: Error and Noise
  • Lecture 5: Training versus Testing
  • Lecture 6: Theory of Generalization
  • Lecture 7: The VC Dimension
  • Lecture 8: Bias-Variance Tradeoff
  • Lecture 9: The Linear Model II
  • Lecture 10: Neural Networks
  • Lecture 11: Overfitting
  • Lecture 12: Regularization
  • Lecture 13: Validation
  • Lecture 14: Support Vector Machines
  • Lecture 15: Kernel Methods
  • Lecture 16: Radial Basis Functions
  • Lecture 17: Three Learning Principles
  • Lecture 18: Epilogue

Taught by

Yaser Abu-Mostafa


4.5 rating, based on 21 Class Central reviews

Start your review of Learning From Data (Introductory Machine Learning)

  • CS1156x: Learning from Data is a 10-week introductory machine learning course offered by Caltech on the edX platform focused on giving students a solid foundation in machine learning theory. Major course topics include the feasibility of learning, l…
  • Professor Yaser Abu-Mostafa created an exceptional course and provides it for free to everyone who wants to take the time and effort to dive into this excellent material. His domain and instructional skills are top of of the bill and the world shoul…
  • Groundbreaking course derived from the original CalTech telecourse, that introduces you to the theoretical and mathematical concepts behind many machine learning algorithms and models. Provides with you no background material, so you must be competent in your prerequisites before beginning. In addition to having a concrete understanding of Statistics, Probability, Linear Algebra & Calculus be prepared to effectively use at least one object oriented or functional programming language.
  • Anonymous
    The best Machine Learning class available for free online, period (I also took Coursera/Stanford's). This class will make you understand very well the principles underlying machine learning. You will do some programming assignments as well but the goal of those assignments is for you to understand what you are doing and why you do it (vs implementing some textbook algorithm for the sake of it). It has the right balance theory/practice. Serious students will love it.
  • I would highly recommend this MOOC to anyone who is interested in machine learning. Every week consists of two lectures (each an hour long) and a problem set of 10 questions. The duration of lectures seem to be long in number only but once you start…
  • Anonymous
    Awesome class!

    The lectures are engaging, and the homework and exams very challenging. Several topics presented have made me excited to pursue them further.

    The only downside was that homework and exam problems were multiple choice, one try only. When I got one wrong, I knew there was something I didn't understand, so I went back and learned from my mistakes until I got the right answer. Still my scores were lousy.

    If you take this and care about your scores, be sure to follow the discussions in the forum (if only I had!).
  • Anonymous
    This MOOC is very technical: it requires knowledge of matrix algebra, calculus, and programming skills. The lectures are great, although some weeks are too theoretical to my taste, and an application is not clear. The professor ,no doubt, is an expert and delivers material in a good pace.s But you have to stay on schedule, missing one week is enough to put you in trouble of making up. Homework takes a while. Plan to spend about 12 hours a week working on it.
  • Giacomo Demarie
    I attended the course starting from September 2016. It is an excellent introduction to Machine Learning, covering both practical and theoretical aspects. The content is challenging and agenda of the course is tight (it is held in parallel with the live Caltech course). However, the staff is very much supportive. In particular, professor Yaser answers personally and promptly to almost all the question in the forum, making this course unique.
  • Rafael Espericueta
    This course is taught by master of machine learning who is also a gifted lecturer, who manages to clearly explain his deep understanding of the concepts. This course will make you work hard, and you need to have the right mathematics background (calculus and linear algebra). But given that, you're in for a treat!
  • This is really an excellent course. It gives a real understanding of the basic concepts and methods in the world of machine learning. But this understanding is achieving through hard work, challenging tasks are available. And complexity is not an end in itself, tasks are chosen so that the solution leads to an improvement in the conceptual understanding of things. The lion's share of tasks requires setting up a computational experiment, so without good programming skills this course can become an excessive load.

    The lecturer talks about the material not dispassionately, but as something very pleasant and interesting for himself. This greatly enhances the effect of perfectly prepared lectures.
  • Monkel
  • Vlad Podgurschi
  • Ant
  • Anonymous
  • HChan

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