Machine Learning

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.

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Created by: Qiang Liu

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Course Description

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.
Mistake Bounded Learning (1 week)
Decision Trees; PAC Learning (1 week)
Cross Validation; VC Dimension; Perceptron (1 week)
Linear Regression; Gradient Descent (1 week)
Boosting (.5 week)
PCA; SVD (1.5 weeks)
Maximum likelihood estimation (1 week) Bayesian inference (1 week)
K-means and EM (1-1.5 week) Multivariate models and graphical models (1-1.5 week) Neural networks; generative adversarial networks (GAN) (1-1.5 weeks)

Advanced Level

Time: 12 Weeks - 8-12 hours per week

Instructor Details

Qiang Liu

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Qiang Liu is an assistant professor of computer science at UT Austin. His research focuses on artificial intelligence and machine learning, especially statistical learning methods for high-dimensional and complex data.