01_cs698ocontents
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cse theoryTRANSCRIPT
CS698oMachine Learning Tools and Techniques
Krithika [email protected]
CSE, IIT Kanpurhttp://web.cse.iitk.ac.in/~cs698o/
Semester 1, 2011-12Mon Thu 1200-1320 @CS102
[email protected] (CSE, IITK) CS698o: Course Contents 2011-2012 1 / 8
General information
Pre-requisites
Linear AlgebraProbability and Statistics
Course based on machine learning tools
Familiarity with programming a mustTools include machine learning functions in MATLAB and WEKAWeekly coding assignments of 1-2 problems on real data
Do assignments individually
Participate in class
Attend class regularlyDiscuss and ask questions
Appointments: email me at [email protected] and put“CS698o” in subject
No extension of deadlines
For illness, follow IITK rules
[email protected] (CSE, IITK) CS698o: Course Contents 2011-2012 2 / 8
Course Material
BooksText Book
Pattern Classification by Duda, Hart and Stork, 2nd Edition
Reference Books
Pattern Recognition by Theodoridis and KoutroumbasPattern Recognition and Machine Learning by BishopThe Elements of Statistical Learning: Data mining, inference andprediction, by Hastie, Tibshirani and Friedman, 2nd EditionStatistical Signal Processing by Scharf
Slides
Class work
[email protected] (CSE, IITK) CS698o: Course Contents 2011-2012 3 / 8
Course Contents
Decision treesBayesian Decision Theory
Minimum error rate classificationMinimax CriterionNeyman Pearson CriterionNormal densities: Discriminant functions and Decision surfacesContinuous and Discrete features, Missing and Noisy features
Maximum Likelihood and Bayesian Parameter EstimationParameter Estimation for Normal DensitySufficient StatisticsExpectation MaximizationProblems of Dimensionality, Training sample size, OverfittingHidden Markov Models
Non-parametric Techniquesk Nearest Neighbor EstimationNearest Neighbor ClassificationDistance MetricsFuzzy Classification
[email protected] (CSE, IITK) CS698o: Course Contents 2011-2012 4 / 8
Course Contents (contd.)
Component Analysis and Discriminant Functions
Principal Component AnalysisFisher Linear DiscriminantGeneralized Linear Discriminant FunctionsLinear Programming AlgorithmsSupport Vector MachinesIndependent Component AnalysisMulti-Dimensional ScalingMulti-category Generalizations
Neural Networks
Feed-forward OperationBack-propagation AlgorithmPractical Techniques to Improve Back-PropagationAdditional Networks
[email protected] (CSE, IITK) CS698o: Course Contents 2011-2012 5 / 8
Course Contents (contd.)
Unsupervised Learning and Clustering
Maximum Likelihood Estimates and Applications to Normal DensitiesCriterion Functions for Clustering and Similarity MetricsHierarchical Clustering
Algorithm Independent Machine Learning
Bias and VarianceResampling for estimating statistics and classifier designEstimating and Comparing ClassifiersCombining Classifiers
[email protected] (CSE, IITK) CS698o: Course Contents 2011-2012 6 / 8
Grading policy
Assignments: 25%
Exams: 25%
Mid-sem: 10%End-sem: 15%
Class participation: 5%
AttendanceDiscussions in class
Quizzes: 10%
Project: 35%
Results 15%Presentation 10%Report 10%
Projects
May be done in groups of size at most 2Deadlines on initial idea, mid-term report and final submission must befollowed
[email protected] (CSE, IITK) CS698o: Course Contents 2011-2012 7 / 8
Grading policy
Assignments: 25%
Exams: 25%
Mid-sem: 10%End-sem: 15%
Class participation: 5%
AttendanceDiscussions in class
Quizzes: 10%
Project: 35%
Results 15%Presentation 10%Report 10%
Projects
May be done in groups of size at most 2Deadlines on initial idea, mid-term report and final submission must befollowed
[email protected] (CSE, IITK) CS698o: Course Contents 2011-2012 7 / 8
Suggestions
What are your interests?
How would you like to learn?
[email protected] (CSE, IITK) CS698o: Course Contents 2011-2012 8 / 8