Jul 03, 2022  
2021-2022 Graduate Academic Catalog 
    
2021-2022 Graduate Academic Catalog [ARCHIVED CATALOG]

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CS 6340 - Machine Learning

3 lecture hours 2 lab hours 4 credits
Course Description
This course provides an introduction to machine learning, which involves the study and construction of algorithms that learn and make predictions based on data.  Topic categories include supervised and unsupervised learning, optimization, and learning theory.  Students reinforce the theoretical content with hands-on tutorial-oriented laboratory exercises for development of representative applications. (prereq: CS 6230 )
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
  • The basic process of machine learning 
  • Differences between supervised and unsupervised learning approaches
  • Analyze and implement a variety of machine learning algorithms (e.g., k-nearest neighbors, logistic regression, SVMs, decision trees) 
  • Confidence in applying, manipulating, and interpreting linear algebra and calculus concepts within the context of machine learning 
  • The concepts of learning theory, i.e., what is learnable, bias, variance, overfitting, curse of dimensionality, splitting data for evaluation of model predictions 
  • Estimating and plotting decision boundaries described by trained models 
  • The role of optimization in machine learning
  • Visualizing, applying, and interpreting metric, cost, loss functions, and loss function derivatives to assess the quality of model predictions and fit to data 
  • Interpreting the effect of and choose appropriate values for hyper-parameters
  • Application of machine learning methods with real-world datasets

Coordinator
Dr. RJ Nowling



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