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Learning From Data (Introductory Machine Learning)

Introductory Machine Learning course covering theory, algorithms and applications. Our focus is on real understanding, not just "knowing."
This course is archived
Future dates to be announced
Estimated 10 weeks
10–20 hours per week
Instructor-paced
Instructor-led on a course schedule
Free
Optional upgrade available

About this course

Skip About this course

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.

At a glance

  • Institution: CaltechX
  • Subject: Computer Science
  • Level: Introductory
  • Prerequisites:

    Basic probability, matrices, and calculus. Familiarity with some programming language or platform will help with the homework.

  • Language: English
  • Video Transcripts: English, Português

What you'll learn

Skip 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

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

About the instructors

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