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:
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You will learn:
- 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.
10 – 20 hours per week
Part Time/Fulltime: –
- Required are the Basic probability, matrices, and calculus. Familiarity with some programming language or platform will help with the homework.
- Yaser S. Abu-Mostafa