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Dec 21, 2024
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CS 4850 - Machine Learning2 lecture hours 2 lab hours 3 credits
Course Description
This course provides a broad introduction to machine learning. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions. Topic categories include supervised, unsupervised, and reinforcement learning. Students will reinforce their learning of machine learning algorithms with hands-on, tutorial-oriented laboratory exercises for development of representative applications. (prereq: MA 262 and CS 2852 )
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
- Understand the concepts of learning theory, i.e., what is learnable, bias, variance, overfitting
- Understand the concepts and application of supervised, unsupervised, semi-supervised, and reinforcement learning
- Understand the application of learned models to problems in classification, prediction, clustering, regression analysis, time-series, game play, and web-scale data
Prerequisites by Topic
- Probability and statistics
- Algorithms
- Programming maturity
Course Topics
- Machine learning theory and applications
- Variance, bias, overfitting
- Learning categories:
- supervised learning
- unsupervised learning
- reinforcement learning
- Representative algorithms from each learning category will be covered:
- gradient descent
- linear regression
- logistic regression
- decision trees
- clustering
- support vector machines
- neural networks for deep learning
Laboratory Topics
- Gradient descent
- Linear regression
- Logistic regression
- Decision trees
- Clustering
- Neural networks for deep learning
Coordinator
Dr. John Bukowy
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