• Length:
    12 Weeks
  • Effort:
    8–12 hours per week
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    Add a Verified Certificate for $1,000 USD

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  • Course Type:
    Instructor-led on a course schedule


Linear Algebra, Probability, Experience programming in Python

About this course

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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.

What you'll learn

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○ Techniques for supervised learning including classification and regression.
○ Algorithms for unsupervised learning including feature extraction.
○ Statistical methods for interpreting models generated by learning algorithms.

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)

Meet your instructors

Qiang Liu
Assistant Professor of Computer Science
The University of Texas at Austin

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Who can take this course?

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