Machine Learning

  • Created: 2015-10-18
  • 1685
Name:Machine Learning
No.:S081202ZJ008Semester:Spring
Hour:40Credit:2.0
Teacher:Qing, Laiyun
Introduction:
 
This course explores the theory and practice of statistical machine learning. The main goal of this class is to introduce you to the ideas and techniques of machine learning, and the probabilistic models that underlie behind them. Topics include parameter estimation, approximate inference, and kernel and nonparametric methods. Applications to regression and categorization problems are illustrated by examples from vision, language, communications, and bioinformatics. This course will discuss the conceptual relationships between these different learning problems, and introduce some of the most practically effective statistical models and computational methods.
Content:
 
Chapter 1: Introduction to Machine Learning
Chapter 2: Review of Probability
Chapter 3: Maximize Likelihood
Chapter 4: Bayesian Inference
Chapter 5: Multiple Variable Normal Distributions
Chapter 6: Decision Theory
Chapter 7: Linear Regression Models
Chapter 8: General Linear Models
Chapter 9: Sparse Models
Chapter 10: Gaussian Processing and Kernel Models
Chapter 11: Sparse Kernel Models
Chapter 12: Markov Chain Motel Carlos
Material:
 

[1] Kevin P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012

[2] Christopher M., Pattern Recognition and Machine Learning, Springer Press, 2006

[3] Wasserman L., All of Statistics: A Concise Course in Statistical Inference, Springer Press, 2005

[4] Hastie T., Tibshirani R. Friedman J., The Elements of Statistical Learning - Data Mining, Inference and Prediction, Springer Press, 2010

References: