General Information Course Id: COSC6342 Machine Learning Time: TU/TH 1-2:30p Instructor: Christoph F. Eick Classroom:AH301 Homepage:

2 What is Machine Learning? Machine Learning is the Machine Learning is the study of algorithms thatstudy of algorithms that improve their performanceimprove their performance at some taskat some task with experiencewith experience Role of Statistics: Inference from a sample Role of Statistics: Inference from a sample Role of Computer science: Efficient algorithms to Role of Computer science: Efficient algorithms to Solve optimization problemsSolve optimization problems Representing and evaluating the model for inferenceRepresenting and evaluating the model for inference

3 Applications of Machine Learning Supervised Learning Supervised Learning ClassificationClassification PredictionPrediction Unsupervised Learning Unsupervised Learning Association AnalysisAssociation Analysis ClusteringClustering Preprocessing and Summarization of Data Preprocessing and Summarization of Data Reinforcement Learning and Adaptation Reinforcement Learning and Adaptation Activities Related to Models Activities Related to Models Learning parameters of modelsLearning parameters of models Choosing/Comparing modelsChoosing/Comparing models Evaluating Models (e.g. predicting their accuracy)Evaluating Models (e.g. predicting their accuracy)

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

Textbooks Textbook: Ethem Alpaydin, Introduction to Machine Learning, MIT Press, Mildly Recommended Textbooks: 1.Christopher M. Bishop, Pattern Recognition and Machine Learning, Tom Mitchell, Machine Learning, McGraw-Hill, 1997.

Grading Spring Exams61-69% 3 Projects and 4HW35-40% Attendance 1% NOTE: PLAGIARISM IS NOT TOLERATED. Remark: Weights are subject to change

Topics Covered in 2011 (Based on Alpaydin) Topic 1: Introduction to Machine Learning Topic 2: Supervised Learning Topic 3: Bayesian Decision Theory (excluding Belief Networks) Topic 5: Parametric Model Estimation 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 Centering on Support Vector Machines Topic 14: More on Kernel Methods Topic 15: Graphical Models Centering on Belief Networks Topic 16: Applications of Machine Learning---Urban Driving, Netflix, etc. Topic 17: Hidden Markov Models Topic 18: Reinforcement Learning Topic 19: Neural Networks Topic 20: Computational Learning Theory Remark: Topics 17, 19, and 20 likely will be only briefly covered or skipped---due to the lack of time.

Course Projects 1.February 2011: Homework1 (available Feb. 6)Individual Project; Classification and Prediction; learn how obtain, use, and evaluate models(available Feb. 10). 2.March/April 2011: Group Project, giving a survey about a subfield of Machine Learning, Homework2 (available after Spring Break) 3.Second Half April 2011: Individual Project (Short); Reinforcement Learning and Adaptation: Learn how to act intelligently in an unknown/changing environment

Course Elements Total: classes lectures lectures 3-4 classes for review and discussing course projects 3-4 classes for review and discussing course projects 2 classes will be allocated for student presentations 2 classes will be allocated for student presentations 2 exams 2 exams Graded and ungraded paper and pencil problems Graded and ungraded paper and pencil problems

Schedule ML Spring 2011 WeekTopic Jan 17 Introduction Jan 24 Supervised Learning Jan 31 Bayesian Decision Theory, Parametric Approaches Feb. 7 Multivariate Methods, Project1, Homework1 Feb. 14 Multivariate Methods, Dim. Reduction Feb. 21 Clustering1 Feb. 28 Non-parametric Methods, Review1 March 7 Decision Trees, Review2, Project2, Midterm Exam March 21 Decision Trees, Clustering2, Reinforcement Learning March 28 Reinforcement Learning April 4 Ensembles, SVM April 11 SVM, Project 3, Project2 SP April 18 Project2 SP, More on Kernels, Project3, Comparing Learners April 28 only May 3 only Review3, Graphical Models, Kaelbling Article, TE Post Analysis Project1, Review 4 April 14, 2011 Green: will use other teaching material

Dates to Remember Dates to rememberEvents March 10 + May 12, 2pExams April 14+19Project2 Student Project Presentations March 15 /17No class (Spring Break) March 24, April 16/18, May 3 Submit Project Report /Software/Deliverable

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  Exam scores will be immediately converted into number grades  We only have 2009 sample exams; I taught this course only once recently

Other UH-CS Courses with Overlapping Contents 1. 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 based Clustering  Medium Overlap: K-means, Decision Trees, Preprocessing/Exploratory DA, AdaBoost Preprocessing/Exploratory DA, AdaBoost  COSC 6343: Pattern Classification  Medium Overlap: all classification algorithms, feature selection—discusses those topics taking a different perspective. a different perspective.