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

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CS 3400 - Machine Learning

3 lecture hours 2 lab hours 4 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 optimization and both supervised and unsupervised methods. Students will reinforce their learning of machine learning algorithms with hands-on, tutorial-oriented laboratory exercises for development of representative applications. (prereq: MA 383  and (CS 2300  or CS 2852 ))
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
  • Understand the basic process of machine learning
  • Understand the concepts of learning theory, i.e., what is learnable, bias, variance, overfitting
  • Understand the concepts and application of supervised and unsupervised learning
  • Analyze and implement basic machine learning algorithms
  • Understand the role of optimization in machine learning
  • Assess the quality of predictions and inferences
  • Apply methods to real world data sets

Prerequisites by Topic
  • Linear algebra
  • Derivative calculus

Coordinator
Dr. John Bukowy



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