Graduate

Courses

Computer Science

B555 Machine Learning

Credits: 3

Prerequisite(s):

Theory and practice of constructing algorithms that learn functions and choose optimal decisions from data and knowledge. Topics include: mathematical/probabilistic foundations, MAP classification/regression, linear and logistic regression, neural networks, support vector machines, Bayesian networks, tree models, committee machines, kernel functions, EM, density estimation, accuracy estimation, normalization, model selection.

Fall 2017


Instructor: Donald Williamson
Time: 4:00PM-5:15PM Mon, Wed
Location: Optometry, Room 111

  • Course History

      Spring 2017


      Instructor: Donald Williamson
      Time: 9:30AM-10:45AM Tue, Thu
      Location: Ballantine Hall, Room 330

      Fall 2016


      Instructor: Martha White
      Time: 4:00PM-5:15PM Mon, Wed
      Location: JHA100
      Course URL (syllabus link or course homepage)

      Spring 2016


      Instructor: Christopher Raphael
      Time: 9:30AM-10:45AM Tue, Thu
      Location: Informatics East, Room 130

      Fall 2015


      Instructor: Martha White
      Time: 11:15AM-12:30PM Mon, Wed
      Location: Informatics East, Room 130
      Course URL (syllabus link or course homepage)

      Spring 2015


      Instructor: Predrag Radivojac
      Time: 1:00PM-2:15PM Tue, Thu
      Location: Lindley Hall, Room 008
      Course URL (syllabus link or course homepage)

      Spring 2014


      Instructor: Predrag Radivojac
      Time: 1:00PM-2:15PM Mon, Wed
      Location: Informatics East, Room 130

      Spring 2013


      Instructor: Predrag Radivojac
      Time: 1:00PM-2:15PM Tue, Thu
      Location: Informatics East, Room 130

      Fall 2011


      Instructor: Predrag Radivojac
      Time: 11:15AM-12:30PM Tue, Thu
      Location: Lindley Hall, Room 008