National University of Sciences and Technology
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CSE 804 Machine Learning
Campus College of E&ME
Programs PG
Session Summer Semester 2016
Course Title Machine Learning
Course Code CSE 804
Credit Hours 3-0
Course Objectives This course has been designed to introduce the graduate students to a very active area of research in Pattern Recognition. In this course the students are introduced with the mathematical background of the tools available that can be used to implement any pattern recognition system.
Detail Content Course Outcome
  • basic knowledge about the key algorithms and theory that form the foundation of machine learning and computational intelligence
  • a practical knowledge of machine learning algorithms and methods so that they will be able to
  • understand the principles, advantages, limitations and possible applications of machine learning
  • identify and apply the appropriate machine learning technique to classification, pattern recognition, optimization and decision problems.
Topics Covered
  • Background Knowledge
  • History of Pattern Recognition
  • Introduction to linear algebra
    • Matrix manipulation
    • Matlab
    • Eigen Values and Eigen vectors
    • Partial Derivatives
  • Optimsation
  • Gradient Descent
  • Lagrange Multipliers
  • Handling Data
    • Types of Data
    • Cross validation
    • PCA
  • Pattern Recognition Algorithms
    • Linear Regressions
    • Logistic Regression
    • Neural networks
    • Support Vector Machines
  • Learning Theory
    • Generalisation
    • Regularisation
    Text/Ref Books Text Books:

    Pattern Recognition, Fourth Edition by SergiosTheodoridis, KonstantinosKoutroumbas

    Reference Books:
    • Pattern Classification (2nd Edition) by RiachardDuda, Peter Hart and David Stork
    • Pattern Recognition and Machine Learning by Christopher Bishop
    • Learning OpenCV (online version available)
    Time Schedule Summer 2015
    Faculty/Resource Person