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