Learning from Data – Data Science Course by California Institute of Technology

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Level: Beginner
Duration: 18 hours
Delivery: Online
Certification: Unknown
Cost: 0
Course Provider: California Institute of Technology


A free, recorded introductory Machine Learning course taught by Caltech Professor Yaser Abu-Mostafa, covering the basic theory, algorithms, and applications, with 8 homework sets and a final exam. Some previous knowledge required.

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  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 18 lectures are about 60 minutes each plus Q&A.

Training Course Content

  • 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

Who Is It For?

This course is geared toward advanced users who want to learn more about machine learning and its applications. Basic probability, calculus and matrices are prerequisite knowledge for this class.





About the Provider

Yaser S. Abu-Mostafa is a Professor of Electrical Engineering and Computer Science at the California Institute of Technology, and Chairman of Machine Learning Consultants LLC. His main fields of expertise are machine learning and computational finance. He is the author of Amazon’s machine learning bestseller Learning from Data. His MOOC on machine learning has attracted more than two million views.

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. He was the founding Program Chairman of the annual conference on Neural Information Processing Systems (NIPS), and a founding member of the IEEE Neural Networks Council. 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 and keynote lectures at international conferences.

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