Learning From Data (Introductory Machine Learning) (edX)

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 story-like fashion:

• What is learning?
• Can a machine learn?
• How to do it?
• How to do it well?
• Take-home lessons.

What you'll learn:

• 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

Syllabus

The topics in the story line are covered by 18 lectures of about 60 minutes each plus Q&A.
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