CSC 5601 - Theory of Machine Learning

4 lecture hours 0 lab hours 4 credits

Course Description
This course provides a broad introduction to machine learning. Theory of 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 decision boundaries, optimization, and both supervised and unsupervised methods. Students will apply the theory to implementing and evaluating machine learning algorithms with hands-on, tutorial-oriented laboratory exercises. This course is open to qualified undergraduates. (prereq: MTH 2130, MTH 2340, and CSC 2621 or instructor consent)

Course Learning Outcomes
Upon successful completion of this course, the student will be able to:

Explain and analyze machine learning models and prediction algorithms (e.g., k-nearest neighbors, logistic regression, SVMs, decision trees) in a precise manner 
Confidence in applying, manipulating, and interpreting linear algebra and calculus concepts within the context of machine learning 
Implement and validate code for a mathematical model or algorithm given as an equation 
Explain the geometric and algebraic interpretations of linear and non-linear decision boundaries and their relationship to error metrics (e.g., accuracy)
Understand the concepts of learning theory, i.e., what is learnable, bias, variance, overfitting, curse of dimensionality, splitting data for evaluation of model predictions 
Estimate and plot decision boundaries described by trained models
Be able to encode non-numerical variables in tabular data as numerical features  
Describe implications of feature representations with respect to linear separability  
Apply approaches for engineering and evaluating new features from existing data  
Derive, visualize, apply, and interpret loss functions and associated derivatives to assess the quality of model predictions and fit to data 
Describe and implement model training in terms of naïve and gradient-driven search problems over parameter space 
Compare and contrast technical requirements and computational efficiency of two unconstrained optimization algorithms (e.g., gradient descent, Newton's method)

Prerequisites by Topic

Linear algebra
Python
NumPy
Functional programming

Coordinator
Dr. John Bukowy

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