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General Information

General Information. Course Id: COSC6342 Machine Learning Time: TU/TH 1-2:30p Instructor: Christoph F. Eick Classroom: AH301 E-mail: ceick@aol.com Homepage: http://www2.cs.uh.edu/~ceick/. What is Machine Learning?. Machine Learning is the

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General Information

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  1. General Information Course Id: COSC6342 Machine Learning Time: TU/TH 1-2:30p Instructor: Christoph F. Eick Classroom: AH301 E-mail: ceick@aol.com Homepage: http://www2.cs.uh.edu/~ceick/

  2. 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

  3. Applications of Machine Learning • Supervised Learning • Classification • Prediction • Unsupervised Learning • Association Analysis • Clustering • Preprocessing and Summarization of Data • Reinforcement Learning and Adaptation • Activities Related to Models • Learning parameters of models • Choosing/Comparing models • Evaluating Models (e.g. predicting their accuracy)

  4. 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”

  5. Textbooks Textbook: EthemAlpaydin, Introduction to Machine Learning, MIT Press, 2010. Mildly Recommended Textbooks: Christopher M. Bishop, Pattern Recognition and Machine Learning, 2006. Tom Mitchell, Machine Learning, McGraw-Hill, 1997.

  6. Grading Spring 2011 • 2 Exams 61-69% • 3 Projects and 4HW 35-40% • Attendance 1% Remark: Weights are subject to change NOTE: PLAGIARISM IS NOT TOLERATED.

  7. 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.

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

  9. Course Elements • Total: 25-26 classes • 18-19 lectures • 3-4 classes for review and discussing course projects • 2 classes will be allocated for student presentations • 2 exams • Graded and ungraded paper and pencil problems

  10. April 14, 2011 ScheduleML Spring 2011 Green: will use other teaching material

  11. Dates to Remember

  12. 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

  13. 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 selection—discusses those topics taking • a different perspective.

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