Principles of Machine Learning

(DSC 391L)

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This course focuses on core algorithmic and statistical concepts in machine learning.

Tools from machine learning are now ubiquitous in the sciences with applications in engineering, computer vision, and biology, among others. This class introduces the fundamental mathematical models, algorithms, and statistical tools needed to perform core tasks in machine learning. Applications of these ideas are illustrated using programming examples on various data sets.

Topics include pattern recognition, PAC learning, overfitting, decision trees, classification, linear regression, logistic regression, gradient descent, feature projection, dimensionality reduction, maximum likelihood, Bayesian methods, and neural networks.

What You Will Learn

  • Techniques for supervised learning including classification and regression
  • Algorithms for unsupervised learning including feature extraction
  • Statistical methods for interpreting models generated by learning algorithms


  • Mistake Bounded Learning (1 week)
  • Decision Trees; PAC Learning (1 week)
  • Cross Validation; VC Dimension; Perceptron (1 week)
  • Linear Regression; Gradient Descent (1 week)
  • Boosting (.5 week)
  • PCA; SVD (1.5 weeks)
  • Maximum likelihood estimation (1 week)
  • Bayesian inference (1 week)
  • K-means and EM (1-1.5 week)
  • Multivariate models and graphical models (1-1.5 week)
  • Neural networks; generative adversarial networks (GAN) (1-1.5 weeks)

Course Availability

  • Spring 2023

Meet Your Instructors

Adam Klivans


Qiang Liu

Assistant Professor

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