sullivannicole / csci5521

Personal coursework from Machine Learning Fundamentals (CSCI 5521, UMN)

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CSCI 5521

Personal coursework from Machine Learning Fundamentals (CSCI 5521, UMN)

The course covers:

Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence.

Professor: Dr. Catherine Qi Zhao, Associate Professor, Computer Science and Engineering (qzhao@cs.umn.edu)

Pre-requisites:

  • Python programming
  • Statistics/probabilities
  • Linear algebra
  • Multivariable calculus

Course texts:

  • Introduction to Machine Learning, Ethem Alpaydin
  • Pattern Recognition and Machine Learning, Christopher M. Bishop

Topics:

  • Introduction
  • Supervised learning
  • Bayesian decision theory
  • Parametric models
  • Dimension reduction
  • Clustering
  • Nonparametric methods
  • Linear discrimination
  • Neural networks, deep learning
  • Kernel machines
  • Decision trees and random forests
  • Graphical models

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Personal coursework from Machine Learning Fundamentals (CSCI 5521, UMN)


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