CS1156x: Learning From Data


Introductory Machine Learning course covering theory, algorithms and applications. Our focus is on real understanding, not just "knowing."

About this Course

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 automatically learn how to perform a desired task based on information extracted from the 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 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

Course Staff

  • Yaser S. Abu-Mostafa

    Dr. Abu-Mostafa is a Professor of Electrical Engineering and Computer Science at the California Institute of Technology. His main fields of expertise are machine learning and computational finance. He is the co-author of Amazon's machine learning bestseller Learning From Data.

    Dr. Abu-Mostafa received the Clauser Prize for the most original doctoral thesis at Caltech. He won various Caltech and national teaching awards, including the Feynman Prize in 1996. He is the co-founder of the Neural Information Processing Systems (NIPS) annual conference, the top international conference on machine learning. He chaired a number of conferences on applying machine learning to finance, including Computational Finance (CF-99). In 2005, the Hertz Foundation established a perpetual graduate fellowship named the Abu-Mostafa Fellowship in his honor.

    Dr. Abu-Mostafa currently serves on a number of scientific advisory boards, and has served as a technical consultant on machine learning for several companies, including Citibank for 9 years. He has numerous technical publications including 3 articles in Scientific American, as well as several keynote lectures at international conferences.

  • 2014年9月25日, 10 星期
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  • 语言: 英语 Gb



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