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California Institute of Technology

Learning from Data (Introductory Machine Learning course)

California Institute of Technology via Independent

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Overview

This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. ML has become one of the hottest fields of study today, taken up by undergraduate and graduate students from 15 different majors at Caltech. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures below 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.

Syllabus

 

  • 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

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Reviews

4.6 rating, based on 10 Class Central reviews

Start your review of Learning from Data (Introductory Machine Learning course)

  • Profile image for Ronny De Winter
    Ronny De Winter @RonnyDeWinter
    Excellent caltech course which runs in parallel with the on-site university class.
    Good theoretical coverage and applied programming exercises. Highly dedicated teacher and teaching assistants, closely following up the discussion forum. recommended for every serious data scientist. One of the best MOOCs I've completed. More elaborated than Andrew Ng's intro to Machine Learning.
  • Profile image for Kallol Roy
    Kallol Roy @kallol
    This course is like a magic. So good. The concepts are beautifully explained. The instructor of the course is gem. I strongly recommend this course.
  • Profile image for Kallol Roy
    Kallol Roy @kallol
    This is one of the best course I have ever taken. The fundamentals are good. Very helpful for the theoreticians and deep learning researchers.
  • HChan
    The best online machine learning course I've taken (I've completed courses by Andrew Ng as well as Hastie and Tibshirani et al), this course covers rigorous theory as well as practical aspects, setting you up for a very solid foundation for future study in machine learning. Assignments are challenging and really require you to understand and engage with the material. Prof Abu-Mostafa's teaching quality is amazing and even highly complex concepts are clearly presented.
  • Profile image for Jason Michael Cherry
    Jason Michael Cherry
  • Asr
  • Harish Ramakrishnan
  • Lars Ahlfors
  • Rafael Prados
  • Colin Khein
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