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Apr 25, 2024
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CS 5830 - Machine Learning
This course covers classification, regression and clustering algorithms, as well as introductory concepts in reinforcement learning. Topics include perceptrons, logistic regression, linear regression, Naive Bayes, nearest neighbors, Support Vector Machines, and Q-learning. The description of the formal properties of the algorithms are supplemented with motivating applications in a wide range of areas including natural language processing, computer vision, bioinformatics, and music analysis.
Requisites:
Credit Hours: 3
Repeat/Retake Information: May not be retaken.
Lecture/Lab Hours: 3.0 lecture
Grades: Eligible Grades: A-F,WP,WF,WN,FN,AU,I
Learning Outcomes:
- Students will be able to implement simple learning algorithms such as kernel perceptrons, ridge regression, nearest neighbors, or Q-learning.
- Students will be able to use gradient descent to solve optimization problems.
- Students will be able to implement and evaluate stochastic gradient descent.
- Students will be able to explain the importance of regularization in machine learning.
- Students will be able to create feature vector representations appropriate for a given problem.
- Students will be able to indicate what machine learning algorithm is appropriate for a given problem.
- Students will be able to use standard techniques such as k-fold cross-validation to conduct rigorous experimental evaluations.
- Students will be able to use third-party machine learning packages.
- Students will be able to solve optimization problems using Lagrange multipliers.
- Students will be able to explain temporal difference learning as gradient descent.
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