Subspace Classifiers
Reference Papers and Slides
Subspace Classifiers and Clustering (zip file)
Pattern Recognition And Reading By Machine
Tutorial on Set Notation
The N-tuple Subspace Classifier: Extensions and Survey
The N-tuple Classifier Part 1, Lecture 2021
Requisite Variety
Generalization of Class Conditional Independence Assumption
Graphical Models
Stacking N-tuple Subspace Classifiers
Class Project (Updated March 23, 2021)
Discrete Bayes Pattern Recognition
N-tuple Classifiers {zip file)
Linear Algebra, Projection Operators, Principal Components
Machine Learning
Fall 2020
Professor Robert M. Haralick
Class Meeting Time: Monday 2-4PM
Office Hours: Monday 4PM-5PM
Main Text
The Elements of Statistical Learning, Hastie, Tibshirani, and Friedman, Springer
Reference Texts
Pattern Classification, Second Edition, Duda, Hart, and Stork, John Wiley, 2001
Probabilistic Graphical Models: Principles and Techniques Daphne Koller and Nir Friedman, The MIT Press, Cambridge MA, 2009
Pattern Recognition, Third Edition, Theodoridis and Koutroumbas, Academic Press, London, 2006.
The Elements of Statistical Learning, Hastie, Tibshirani, and Friedman, Springer
Data Clustering: Algorithms and Applications, Aggarwal and Reddy, CRC
Course In Machine Learning, Hal Daume,
Introduction To Machine Learning, Alex Smola and S.V.N. Vishwanathan, Cambridge University Press, 2008
Neural Networks For Pattern Recognition, Christopher Bishop, Clarendon Press, 1995
Linear Algebra (3rd Edition), Serge Lang, Springer, 1987
Reference Papers
Useful Things To Know About Machine Learning, Pedro Domingo, CACM, 2012
On the Surprising Behavior of Distance Metrics in High Dimensional Space, Aggarwal, Hinneburg, and Keim, ICDT, 2001
The N-Tuple Classifier: Too Good to Ignore, Michal Morciniec and Richard Rohwer
A Stochastic Search Algorithm To Optimize An N-tuple Classifier by Selecting Its Inputs, Hannan Azhar and Keith Dimond
Optimizing Memory Usage In N-tuple Neural Networks, R.J. Mitchell, J.M. Bishop, P.R. Minchinton
Theoretical Analysis and Improved Decision Criteria for the N-Tuple Classifier T.M. Jorgensen and C. Linneberg
Texture Classification Using the N-Tuple Pattern Recognition L Hepplewhite and T.J. Stonham
Global K-Means, A. Likas, N. Vlassis, J. Verbeek
K-Means++: The Advantages of Careful Seeding, D. Arthur, S Vassilvitskii
Subspace Method in Pattern Recognition, Satosi Watanabe
Fatal Abstract
Subspace Classifiers and Clustering (zip file)
Syllabus
Lecture Slides
- Discrete Bayes Pattern Recognition (Lecture August 31) (Updated October 8)
- Midterm Project (updated October 16) (Lecture September 14)
- Read p 1-8: Mathematical Language
- Requisite Variety(Lecture September 21)
- Read: Useful Things To Know About Machine Learning, Pedro Domingo, CACM, 2012
- Read: pages 241-249 The Elements of Statistical Learning Hastie, Tibshirani, and Friedman, Springer
- Some Subspace Classifiers (Up through slide 28 Lecture September 14, September 21)
- Read: p 241-249, The Elements of Statistical Learning, Hastie, Tibshirani, and Friedman, Springer
- Read: p 19-21, p 58-62, Course In Machine Learning, Hal Daume
- Read: Subspace Method in Pattern Recognition Satosi Watanabe
- Introduction To Subspace Classifiers: Projection Operators (Lecture September 21, 29 updated October 2,2020)
- Ntuple Classifier (Lecture October 5) (Updated October 5)
- Decision Trees (Lecture October 14) (Updated October 16)
- Final Project (Lecture October 14)
- Logistic Regression (Lecture October 19) (Updated October 29)
- Parametric Probability Models (Lecture October 26)
- Probability Models (Lecture November 2)
- Conditional Expected Gain and Probabilistic Decision Rules (Lecture November 9)
- Maximin Decision Rule (Lecture November 9)(Updated November 16)
- Unimodal Search (Lecture November 16)
- Quantization (Lecture November 23)
- Generalization of Class Conditional Independence Assumption (Lecture November 30)
- Making Decisions in Context (Lecture November 30)
- Clustering (Lecture December 7)
Grading
- Midterm Project 35%
- Final Project 55%
- Class Attendance 10%
Midterm and Final Report Writeup Instructions (updated October 6)
Challenges
Kaggle Competitions
Toward Successful Participation in Machine Learning Contests
Music MIDI files
Python
A KAR file is just like a MIDI file except it has text for the verses.
You can change the file from *.KAR to *.MIDI and read it with the reader.
