2020-2021 Undergraduate Academic Catalog 
    
    Oct 26, 2021  
2020-2021 Undergraduate Academic Catalog [ARCHIVED CATALOG]

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CS 4850 - Machine Learning

2 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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