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| MA 37400 - Mathematical Foundations For Machine Learning |
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Associated Term:
Spring 2023
Learning Outcomes: 1. Identify a machine learning related data-science problem, formulate questions, visualize the data, and identify features, and conduct statistical inference, and derive prediction algorithms. 2. Know how to use a computer programming tool (e.g. Python or Matlab) to draw samples from important distributions. 3. Understand linear machine learning models for regression problems; know how to use a computer programming tool (e.g. Python or Matlab) to implement the linear machine learning models for regression problems. 4. Understand linear machine learning models for classification problems; know how to use a computer programming tool (e.g. Python or Matlab) to implement the linear machine learning models for classification problems. 5. Know how to build kernel methods; in particular, Gaussian process methods, and how to use a computer programming tool (e.g. Python or Matlab) to implement kermel methods. 6. Understand the concept of mixture models and the Expectational-Maximization algorithm and how to use a computer programming tool (e.g. Python or Matlab) to implement mixture models and the Expectational-Maximization algorithm. 7. Understand the sampling methods and perform computations for Markov Chain Monte Carlo. Required Materials: Technical Requirements: |
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