Tools from machine learning are now ubiquitous in the sciences with applications in engineering, computer vision, and biology, among others. This class introduces the fundamental mathematical models, algorithms, and statistical tools needed to perform core tasks in machine learning. Applications of these ideas are illustrated using programming examples on various data sets.
Topics include pattern recognition, PAC learning, overfitting, decision trees, classification, linear regression, logistic regression, gradient descent, feature projection, dimensionality reduction, maximum likelihood, Bayesian methods, and neural networks.
Machine Learning
The University of Texas at Austin
via edX
This course focuses on core algorithmic and statistical concepts in machine learning. Topics include pattern recognition, PAC learning, overfitting, decision trees, classification, linear regression, logistic regression, gradient descent, feature projection, dimensionality reduction, maximum likelihood, Bayesian methods, and neural networks.
Instructor(s)
Qiang Liu
The University of Texas at Austin
via edX
Free (audit)
English
Paid Certificate Available
Estimated 12 weeks
Instructor paced
Advanced
Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, German, Russian, English, Hebrew, Spanish, Hindi, Japanese