CSC 5601 - Theory of Machine Learning

• 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)  

• Linear algebra
• Python
• NumPy
• Functional programming