Learning From Data (Introductory Machine Learning) (edX)

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Learning From Data (Introductory Machine Learning) (edX)
Course Auditing
Categories
Effort
Certification
Languages
Basic probability, matrices, and calculus. Familiarity with some programming language or platform will help with the homework.
Misc

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Learning From Data (Introductory Machine Learning) (edX)
Introductory Machine Learning course covering theory, algorithms and applications. Our focus is on real understanding, not just "knowing."

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

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



MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

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

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.