COMP_SCI 349: Machine Learning

Quarter Offered

Summer : 2-3:20 TuTh ; Alam
Fall : 9:30-10:50 MW or 8-9:20 TuTh ; Pardo & Wood-Doughty
Winter : 12:30-1:50 MW ; Alam
Spring : 9:30-10:50 MW ; Wood-Doughty


Prerequisites: COMP_SCI grad standing OR (COMP_SCI 214 and (MATH 240-0 or GEN_ENG 205-1 or GEN_ENG 206-1) and (IEMS 201-0 or IEMS 303-0 or ELEC_ENG 302-0 or STAT 210-0 or MATH 310-1)



Machine Learning is the study of algorithms that improve automatically through experience. Topics covered typically include Bayesian Learning, Decision Trees, Genetic Algorithms, Neural Networks.

  • This course satisfies the AI Breadth Requirement.


  • Summer, Fall, Spring & Winter Section:Online readings provided by the course instructor.

REFERENCE TEXTBOOKS: Selected papers from journals and conferences presenting research on Machine Learning

COURSE COORDINATOR: Prof. Doug Downey & Prof. Bryan Pardo
COURSE INSTRUCTOR: Prof. Pardo (Fall), Mohammed A. Alam (Summer & Winter) & Zach Wood-Doughty (Spring)

COURSE GOALS: To expose students to concepts and methods in machine learning. To give students a basic set of machine learning tools applicable to a variety of problems. To teach students critical analysis of machine learning approaches so that the student can determine when a particular technique is applicable to a given problem and apply or implement that technique.


This is an example set of topics. The exact subset will vary depending on year.

  • Decision Tree Learning
  • Nonlinear Regression
  • Artificial Neural Networks
  • Evaluating Hypotheses
  • Bayesian Learning
  • Computational Learning Theory
  • Instance-Based Learning
  • Genetic Algorithms
  • Learning Sets of Rules
  • Reinforcement Learning
  • Clustering

HOMEWORK ASSIGNMENTS: Reading assignment from the Machine Learning Literature. Coding assignments implementing machine learning algorithms, and experiments testing ML algorithms on real-world data.

LABORATORY ASSIGNMENTS: There will be several lab assignments. Students will be required to implement machine learning algorithms and analyze their performance on example sets of data. Example algorithms include: feed-forward multilayer neural networks, decision trees, hidden Markov models, automated clustering techniques.

GRADES: Will be based on a combination of problem sets, reading assignments and programming assignments.