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Introduction to Machine Learning

Machine Learning 101: Introduction to Machine Learning

Introductory Machine Learning course covering theory, algorithms, and applications.

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. 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: Machine Learning 101 Introduction to Machine Learning

What is learning?

Can a machine learn?

How to do it?

How to do it well?

Take-home lessons.

Outline of this Course

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

This course has some videos on youtube that have Creative Commons Licence (CC).