631I & IT 638 Introduction to Pattern Recognition

  • Instructor: Prof. Sung-Hyuk Cha


  • CRN: 56973 & 50801

  • Textbook: Pattern Classification (2nd. Edition) by Duda, Hart and Stork

  • Description:
    Pattern Recognition techniques are useful in many applications of computer science and information systems, such as information retrieval, data mining, artificial intelligence and image processing. This course is an introduction to the foundation of pattern recognition algorithms.

    Topics to be studied: data structures for pattern representation, feature extraction and selection, parametric and non-parametric classification, supervised and non-supervised learning, clustering, decision trees, nearest neighbor, artificial neural networks, generic algorithm, and hidden Markov models. Applications of various classification techniques will be demonstrated by several on-going handwriting, graphics, and speech recognition projects.

  • Lectures: will be on the http://blackboard.pace.edu
    Blackboard Login Procedures for Registered Students are available here

  • Schedule: (tentative)

    Week Reading assignments
    1 (9/6) Ch 1 Introduction: all sections
    2 (9/13) Ch 2 Bayes Decision Theory: 2.1~2.4
    3 (9/20) Ch 2 Bayes Decision Theory: 2.5, 2.6, 2.9
    4 (9/27) Ch 3 Maximum-Likelihood and Bayesian Parameter Estimation: 3.1~3.4
    5 (10/4) Ch 3 Maximum-Likelihood and Bayesian Parameter Estimation: 3.5, 3.7~3.8
    6 (10/11) Ch 4 Nonparametric Techniques: 4.1~4.3
    7 (10/18) Ch 4 Nonparametric Techniques: 4.4~4.6
    8 (10/25) Ch 5 Linear Discriminant Functions: 5.1~.5.4
    9 (11/1) Ch 5 Linear Discriminant Functions: 5.5~.5.9
    10 (11/8) Ch 6 Multilayer Neural Networks: 6.1 ~6.6
    11 (11/15) Ch 8 Nonmetric Methods: 8.1~8.4
    12 (11/22) Thanx giving break
    13 (11/29) Ch 10 Unsupervised Learning & Clustering: 10.1~10.5
    14 (12/6) Ch 10 Unsupervised Learning & Clustering: 10.6, 10.7, 10.9
    15 (12/13) Final report preparation week

  • Evaluation:
    • Discussion Participation (50%):
    • Weekly Assignments (50%):There will be roughly 12 assignments.

  • Student Responsibilities