Learning From Data (Introductory Machine Learning) Caltech course starts on edX Sep 18
This introductory Machine Learning course taught by top Caltech professor AbuMostafa covers theory, algorithms and applications, with focus on real understanding. Starts Sep 18, 2016 on edX.
By Yaser AbuMostafa, Caltech.
Learning From Data (Introductory Machine Learning), a toprated Caltech course by a leading Caltech professor Yaser AbuMostafa, 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 storylike fashion:
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:
Related:
Learning From Data (Introductory Machine Learning), a toprated Caltech course by a leading Caltech professor Yaser AbuMostafa, 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 storylike fashion:
 What is learning?
 Can a machine learn?
 How to do it?
 How to do it well?
 Takehome lessons.
 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
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
Related:
 edX "Learning From Data" Caltech course, 2014
 Coursera / Stanford Mining Massive Datasets MOOC
 Caltech Prof. AbuMostafa on his MOOC course "Learning from Data" and Machine Learning
 Caltech Prof. AbuMostafa on what he learned from his MOOC course "Learning from Data", part 2
 Learning from Data, Caltech Free Online Course, now with captions in 40 languages
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