• Basic information
• Learning outcomes
• Contents
• Assessment
• Workload

Learning outcomes

Upon successful completion of this course, students will be able to:

1. Analyze a given pattern recognition problem, and determine which algorithm to use.

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Electronics Engineering
   2. Formulate and solve advanced engineering problems
   3. Acquire scientific knowledge

   Method of assessment

   1. Written exam
2. Modify existing algorithms to engineer new algorithms to solve a particular problem at hand.

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Electronics Engineering
   2. Formulate and solve advanced engineering problems

   Method of assessment

   1. Written exam
   2. Homework assignment
3. Validate the pattern recognition techniques presented in more advance texts as well as journal articles.

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Electronics Engineering
   2. Formulate and solve advanced engineering problems
   3. Acquire scientific knowledge
   4. Design and conduct research projects independently
   5. Effectively express his/her research ideas and findings both orally and in writing
   6. Defend research outcomes at seminars and conferences

   Method of assessment

   1. Seminar/presentation
   2. Term paper
4. Implement pattern recognition algorithms MATLAB (or an equivalent programming language) based on a given algorithmic description or theory

   Contribution to Program Outcomes

   1. Formulate and solve advanced engineering problems
   2. Develop an awareness of continuous learning in relation with modern technology

   Method of assessment

   1. Laboratory exercise/exam
   2. Term paper

Contents
Week 1:	Introduction of basic concepts in pattern recognition
Week 2:	Bayes decision theory Discriminant functions and decision surfaces
Week 3:	Bayesian classification for normal distribution
Week 4:	Estimation of unknown probability density functions: Maximum likelihod parameter estimation Maximum a posteriori probabiliy estimation Bayesian Inference Expectation maximization Mixture models
Week 5:	Estimation of unknown probability density functions: Parzen windows Nearest neighbor estimation The nearest neighbor rule
Week 6:	Midterm exam
Week 7:	Linear Discriminant Functions The Perceptron Algorithm Least Squares Methods
Week 8:	Fisher's Linear Discriminant PCA ICA
Week 9:	Unsupervised learning and clustering: C-Means Fuzzy C-Means
Week 10:	Similarity Measures Criterion Functions for Clustering Hierarchical Clustering
Week 11:	Feature Generation and Feature Selection
Week 12:	Support Vector Machines
Week 13:	Hidden Markov Model
Week 14:	Presentation of term project to class
Week 15*:	General review
Week 16*:	Final exam
Textbooks and materials:	S. Theodoridis and K. Koutroumbas, “Pattern recognition”, Fourth Edition, Academic Press, 2009.
Recommended readings:	Duda, Hart and Stork, Pattern Classification, Wiley Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press Ewens & Grant, Statistical Methods in Bioinformatics, Springer Alpaydin, Introduction to Machine Learning, MIT Press Haykin, Neural Networks and Learning Machines, Prentice Hall Devroye, Gyorfi & Lugosi, A Probabilistic Theory of Pattern Recognition, Springer
	* Between 15th and 16th weeks is there a free week for students to prepare for final exam.

Assessment
Method of assessment	Week number	Weight (%)
Mid-terms:	6	20
Other in-term studies:		0
Project:	4,8	35
Homework:	2,3,7,8,9,11	15
Quiz:		0
Final exam:	16	30
	Total weight:	(%)

Workload
Activity	Duration (Hours per week)	Total number of weeks	Total hours in term
Courses (Face-to-face teaching):	3	14
Own studies outside class:	4	14
Practice, Recitation:	0	0
Homework:	6	6
Term project:	14	2
Term project presentation:	1	1
Quiz:	0	0
Own study for mid-term exam:	12	1
Mid-term:	1	1
Personal studies for final exam:	14	1
Final exam:	2	1
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)