# H I G H L I G H T S

## A real Caltech course, __not__ a watered-down version

**More than 4 Million views on YouTube & iTunes**

__Featured on__

**Free**, introductory*Machine Learning*online course (MOOC)- Taught by Caltech Professor Yaser Abu-Mostafa [article]
- Lectures recorded from a live broadcast, including Q&A
- Prerequisites:
__Basic__probability, matrices, and calculus - 8 homework sets and a final exam
- Discussion forum for participants
- Topic-by-topic video library for easy review

*Take the course at your own pace*

## Outline

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 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 below 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. The content of each lecture is color coded:

**theory; mathematical**

technique; practical

analysis; conceptual

technique; practical

analysis; conceptual

*Place the mouse on a lecture title for a short description*

- 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**

You can also look for a particular topic within the lectures in the Machine Learning Video Library.

## Live Lectures

This course was broadcast live from the lecture hall at Caltech in April and May 2012. There was no 'Take 2' for the recorded videos. The lectures included live Q&A sessions with online audience participation. Here is a sample of a live lecture as the online audience saw it in real time.