Course No: 0907542
Course Name: Pattern Recognition

Handouts:

• Course Syllabus (pdf)
• Project Report Guidelines (pdf) New
• Slides:
• Course Introduction (ppt)
• Introduction (ppt)
• Bayes Classifiers (ppt)
• Linear Classifiers (ppt)
• Nonlinear Classifiers (ppt)
• Feature Selection (ppt)
• Feature Generation (ppt)
• Template Matching (ppt)
• Context Dependent Classification (ppt)
• System Evaluation (ppt)
• Clustering Algorithms (ppt)
• Clustering Techniques (ppt)

Demonstrations:

• Matlab Pattern Classification 1
• Matlab Pattern Classification 2
• Feature Extraction
• Feature Selection (Slides)
• Matlab Pattern Classification 3

Suggested problems:

• Chapter 2: 2.2, 2.7, 2.8, 2.12, 2.31
• Chapter 3: 3.4, 3.6, 3.9 (Some problems require programming)
• Chapter 4: 4.1, 4.3

Important Links:

• Textbook Web Site
• Pattern Classification (2nd ed) by Richard O. Duda, Peter E. Hart and David G. Stork
• Pattern Recognition Course (CSE802) by Dr. Anil Jain
• Machine Learning Course (CS 229) by Dr. Andrew Ng

Links to Items Used in the Last Lecture:

• Pattern Recognition Presentation by Nick Lund
• 15 Current Technologies A Child Born Today Will Never Use
• Introducing the Google’s Knowledge Graph
• Do Androids Dream of Stealing Your Job?
• Future Technology Watch your day in 2020

Solutions:

• Midterm Exam (pdf)

Grades sheet (pdf) that shows the number of absents as of 18/4/2012 and the midterm exam and project results. Also, the marks as entered in ACAD (pdf).

Last update on 17/5/2012, 1:34 pm.