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Silver BlogLearning From Data (Introductory Machine Learning) Caltech course starts on edX Sep 18

This introductory Machine Learning course taught by top Caltech professor Abu-Mostafa covers theory, algorithms and applications, with focus on real understanding. Starts Sep 18, 2016 on edX.

By Yaser Abu-Mostafa, Caltech.

Learning From Data Learning From Data (Introductory Machine Learning), a top-rated Caltech course by a leading Caltech professor Yaser Abu-Mostafa, covers theory, algorithms and applications. Course focus is on real understanding, not just "knowing."

Course starts on edX on Sep 18, 2016 and will last 10 weeks.

About the course: 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.
What you'll learn
  • Identify basic theoretical principles, algorithms, and applications of Machine Learning
  • Elaborate on the connections between theory and practice in Machine Learning
  • Master the mathematical and heuristic aspects of Machine Learning and their applications to real world situations
Register here.

The course lecture videos have been very popular with over 2 million views on the Caltech YouTube and iTunes channels.

Interestingly, the most popular lecture videos (as of Sep 17, 2016) are:
  • Lecture 01 - The Learning Problem, 383K views
  • Lecture 10 - Neural Networks, 213K views
  • Lecture 02 - Is Learning Feasible? 180K views
  • Lecture 03 - The Linear Model I, 135K views
  • Lecture 14 - Support Vector Machines, 133K views


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