Introductory Machine Learning course covering theory. algorithms and applications.
Introduction to machine learning
Machine learning 101: Introduction to machine learning .
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 cover the mathematical as well as the heuristic aspects. The lectures below follow each other in a story -like fashion:
- What is leaning
- 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
- Lectures 2: Is learning Feasible ?
- Lectures 3: The Linear Model I
- Lectures 4: Error and Noise
- Lectures 5: Training versus Testing
- Lectures 6 Theory of Generalization
- Lectures 7:The VC Dimension
- Lectures 8: Bias- Variance Tradeoff
- Lectures 9: The Linear model II
- Lectures 10: Neural Networks
- Lectures 11: Overfitting
- Lectures 12 : Regularization
- Lectures 13: Validation
- Lectures 14: Support Vector Machines
- Lectures 15: Kernel methods
- lectures 16: Radial Basics functions
- Lectures 17: three learning principle
- Lectures 18: Epilogue
This course has some videos on youtube that has creative commen licence(cc)