1. General Information Course Id: COSC6342 Machine Learning Time: MO/WE 2:30-4p Instructor: Christoph F. Eick Classroom:SEC 201 E-mail: ceick@aol.comceick@aol.com Homepage: http://www2.cs.uh.edu/~ceick/http://www2.cs.uh.edu/~ceick/

2. 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 Learning, representing and evaluating models for inferenceLearning, representing and evaluating models for inference

3. Example of a Decision Tree Model categorical continuous class Refund MarSt TaxInc YES NO YesNo Married Single, Divorced < 80K> 80K Splitting Attributes Training DataDecision Tree Model f: {yes,no}{married,single,divorced} +  {yes,no} Classification Model in General:

4. 4 Machine Learning Tasks Supervised Learning Supervised Learning ClassificationClassification PredictionPrediction Unsupervised Learning and Summarization of Data Unsupervised Learning and Summarization of Data Association AnalysisAssociation Analysis ClusteringClustering Preprocessing Preprocessing 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)

5. 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”

6. Textbooks Textbook: Ethem Alpaydin, Introduction to Machine Learning, MIT Press, Second Edition, 2010. Mildly Recommended Textbooks: 1.Christopher M. Bishop, Pattern Recognition and Machine Learning, 2006. 2.Tom Mitchell, Machine Learning, McGraw-Hill, 1997.

7. Grading Spring 2014 2 Exams58-62% 3 Projects and 2HW38-41% Attendance 1% NOTE: PLAGIARISM IS NOT TOLERATED. Remark: Weights are subject to change

8. Topics Covered in 2014 (Based on Alpaydin) Topic 1: Introduction to Machine Learning Topic 18: Reinforcement 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: Success Stories of Machine Learning Topic 17: Hidden Markov Models 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. For Topic 16 your input is appreciated!

9. Course Elements Total: 26-27 classes 18-19 lectures 18-19 lectures 3 course projects 3 course projects 2-3 classes for review and discussing course projects 2-3 classes for review and discussing course projects 1-2 classes will be allocated for student presentations 1-2 classes will be allocated for student presentations 3 40 minutes reviews 3 40 minutes reviews 2 exams 2 exams Graded and ungraded paper and pencil home problems Graded and ungraded paper and pencil home problems Course Webpage: http://www2.cs.uh.edu/~ceick/ML/ML.html Course Webpage: http://www2.cs.uh.edu/~ceick/ML/ML.html http://www2.cs.uh.edu/~ceick/ML/ML.html

10. 2014 Plan of Course Activities 1.Through March 15: Homework1; Individual Project1 (Reinforcement Learning and Adaptation: Learn how to act intelligently in an unknown/changing environment); Homework2. 2.We., March 5: Midterm Exam 3.March 16-April 5: Group Project2 (TBDL). 4.April 6-April 26: Homework3, Project3 (TBDL) 5.Mo., May 5, 2p: Final Exam

11. Schedule ML Spring 2013 WeekTopic Jan 14 Introduction Jan 16 Introduction / Supervised Learning Jan 21 Bayesian Decision Theory, Parametric Approaches Jan. 23 Multivariate Methods, Homework1 Jan. 28 Multivariate Methods, Dim. Reduction, Project1 Jan. 30 Clustering1 Feb. 5 Non-parametric Methods, Review1 … Decision Trees, Review2, Project2, Midterm Exam … Decision Trees, Clustering2, Reinforcement Learning … Reinforcement Learning … Ensembles, SVM … SVM, Project 3, Project2 SP … Project2 SP, More on Kernels, Project3, Comparing Learners … Review3, Graphical Models, Kaelbling Article, TE Post Analysis Project1, Review 4 Remark: Schedule is the same as in 2013, except reinforcement learning will covered after the introduction. Green: will use other teaching material

12. Dates to Remember March 5 (or March 17) + May 5, 2p Exams April 6+8??Project2 Student Project Presentations Jan. 20, March 10/12No class (Spring Break) Feb. 23, April 3/5, April 26 Submit Project Report /Software/Deliverable

13. 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, 2011 and 2013 sample exams; I taught this course only three times recently.

14. 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.

15. Purpose of COSC 6342 Machine Learning is the study of how to build computer systems that learn from experience. It intersects with statistics, cognitive science, information theory, artificial intelligence, pattern recognition and probability theory, among others. The course will explain how to build systems that learn and adapt using real- world applications. Its main themes include: Learning how to create models from examples that classify or predict. Learning in unknown and changing environments Theory of machine learning Preprocessing Unsupervised learning and other learning paradigms

16. Course Objectives COSC 6342 Upon completion of this course, students will know what the goals and objectives of machine learning are will have a basic understanding on how to use machine learning to build real-world systems will have sound knowledge of popular classification and prediction techniques, such as decision trees, support vector machines, nearest-neighbor approaches and regression. will learn how to build systems that explore unknown and changing environments will get some exposure to machine learning theory, in particular how learn models that exhibit high accuracies. will have some exposure to more advanced topics, such as ensemble approaches, kernel methods, unsupervised learning, feature selection and generation, density estimation.