General Information

1
General Information
Course Id COSC6342 Machine Learning Time
TU/TH 10a-1130a Instructor Christoph F.
Eick Classroom AH123 E-mail
ceick_at_aol.com Homepage
http//www2.cs.uh.edu/ceick/

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What is Machine Learning?

• Machine Learning is the
• study of algorithms that
• improve their performance
• at some task
• with experience
• Role of Statistics Inference from a sample
• Role of Computer science Efficient algorithms to
• Solve optimization problems
• Representing and evaluating the model for
  inference

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Applications of Machine Learning

• Supervised Learning
• Classification
• Prediction
• Unsupervised Learning
• Association Analysis
• Clustering
• Preprocessing and Summarization of Data
• Reinforcement Learning
• Activities Related to Models
• Learning parameters of models
• Choosing/Comparing models

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Prerequisites

• Background
• Probabilities
• Distributions, densities, marginalization
• Basic statistics
• Moments, typical distributions, regression
• Basic knowledge of optimization techniques
• Algorithms
• basic data structures, complexity
• Programming skills
• We provide some background, but the class will be
  fast paced
• Ability to deal with abstract mathematical
  concepts

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Textbooks

• Textbook
• Ethem Alpaydin, Introduction to Machine Learning,
  MIT Press, 2004.
• Recommended Textbooks
• Christopher M. Bishop, Pattern Recognition and
  Machine Learning, 2006.
• Tom Mitchell, Machine Learning, McGraw-Hill,
  1997.

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Grading
3 Exams 67-70 Project 18-24 Homeworks
10-15 Attendance
1-2  
Remark Weights are subject to change
NOTE PLAGIARISM IS NOT TOLERATED.
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Topics Covered in 2009 (Based on Alpaydin)

• Topic 1 Introduction
• Topic 2 Supervised Learning
• Topic 3 Bayesian Decision Theory (excluding
  Belief Networks)
• Topic 4 Using Curve Fitting as an Example to
  Discuss Major Issues in ML
• Topic 5 Parametric Model Selection
• Topic 6 Dimensionality Reduction Centering on
  PCA
• Topic 7 Clustering1 Mixture Models, K-Means and
  EM
• Topic 8 Non-Parametric Methods Centering on kNN
  and Density Estimation
• Topic 9 Clustering2 Density-based Approaches
• Topic 10 Decision Trees
• Topic 11 Comparing Classifiers
• Topic 12 Combining Multiple Learners
• Topic 13 Linear Discrimination
• Topic 14 More on Kernel Methods
• Topic 15 Naive Bayes' and Belief Networks
• Topic 16 Hidden Markov Models
• Topic 17 Sampling

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Course Project

• The project will center on the application of
  machine learning techniques
• to a challenging problem. It will be
  conducted in the window Feb. 12-April 11.
• You can either conduct some novel experiments by
  applying machine learning
• algorithm(s) to a challenging machine
  learning task or attempt a theoretical
• analysis.
• Findings of the project will be summarized in a
  report and in a brief presentation.
• The report must include a short survey of
  related work with the corresponding list
• of references.

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Tentative ML Spring 2009 Schedule
March 31, 2009
Week Topic
Jan 20 Introduction
Jan 27 Supervised Learning/Bayesian Decision Theory
Feb. 3 Curve Fitting/Model Estimation---Parametric Approaches
Feb. 10 Model Estimation---Parametric Approaches
Feb. 17 Parametric Approaches/Clustering1
Feb. 24 Clustering1/Non-param Methods
March 3 Non-Param Methods/Exam1
March 10 Clustering2/Dim. Reduction,Decision Trees
March 24 Dim. Reduction DecisionTrees /Exam2
March 31 SVMs/Kernel Methods Ensemble Methods
April 7 Comparing Classifiers/Group1 Presentations
April 14 Group2 Presentations/TBDL
April 21 Reinforcement Learning/possibly Belief Networks
April 28 Review/Exam3

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Course Elements

• Total 25-26 classes
• 18 lectures
• 2-3 classes for review and discussing homework
  problems
• 2 classes will be allocated for student
  presentations
• 3 exams
• homeworks
• individual graded
• group graded
• not-graded (solutions will be discussed in
  lecture 7-9 days later).

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Dates to Remember
Dates to remember Events
March 5, March 26, April 30 Exams
April 9 and 14 Student Project Presentations
March 17 /19 No class (Spring Break)
April 13(Group1)/April 15(Group2) 11p Submit Project Report /Software/

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Exams

• Will be open notes/textbook
• Will get a review list before the exam
• Exams will center (80 or more) on material that
  was covered in the lecture
• There will be a review prior to the second and
  third exam first exam will mostly
• center on basics.
• Exam scores will be immediately converted into
  number grades
• No sample exams sorry I havent taught this
  course for a long time

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Other UH-CS Courses with Overlapping Contents

• COSC 6368 Artificial Intelligence
• Strong Overlap Decision Trees, Bayesian Belief
  Networks
• Medium Overlap Reinforcement Learning
• COSC 6335 Data Mining
• Strong Overlap Decision trees, SVM, kNN,
  Density-
• based Clustering
• Medium Overlap K-means, Decision Trees,
• Preprocessing/Exploratory DA, AdaBoost
• COSC 6343 Pattern Classification
• Medium Overlap all classification algorithms,
  feature selectiondiscusses those topics taking
• a different perspective.