National University of Sciences and Technology
Home | Back
SE -807 Machine Learning
Campus MCS
Programs PG
Session Summer Semester 2016
Course Title Machine Learning
Course Code SE -807
Credit Hours 3
Pre-Requisutes A course in algorithm design and computing, statistics and linear algebra
Course Objectives
Detail Content
  1. Introduction, induction, Types of machine learning
  2. Nearest Neighbors
  3. Decision Trees
  4. Neural Networks
  5. Learning Conjunctions
  6. Linear and non Linear saparability
  7. Evaluating learning algorithms
  8. Regression and classification
  9. Active learning
  10. Feature selection
  11. Estimation, bias, variance, loss, Empirical risk, maximum likelihood
  12. Generalization, overfitting
  13. Regularization
  14. linear regression, additive models
  15. Generalized Linear Models
  16. Neural networks
  17. Support Vector Machine (SVM)
  18. Boosting
  19. Mixture models, mixtures of experts
  20. Kernel density estimation
  21. Markov chain/processes
  22. Hidden Markov Models (HMM)
  23. Belief networks, Markov random fields
  24. Cross-validation
Text/Ref Books

a.         Duda, R. O. Hart, P. E. D., and Stork, G. Pattern Classification. New York, NY: Wiley, 2000. ISBN: 0471056693

b.         Pattern Recognition and Machine Learning (Information Science and Statistics), by Christopher M. Bishop, Springer; 1 edition (August 17, 2006)

c.         Machine Learning (Hardcover), by Tom M. Mitchell, McGraw-Hill

Time Schedule Summer Semester 2015
Faculty/Resource Person A/P Dr Seemab Latif
Discipline: Computer Science
Specialization: Computational Linguistics and Text Mining