PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
CENG 420	MACHINE LEARNING & PATTERN RECOGNITION	3 + 0	8th Semester	5

COURSE DESCRIPTION

Course Level	Bachelor's Degree
Course Type	Elective
Course Objective	Purpose of this course is to teach fundamental concepts of machine learning and pattern recognition.
Course Content	Introduction to machine learning, supervised learning, regression, model order and generalization properties, Bayes decision theory, maximum likelihood method, distance functions, multivariable models and regression, dimensionality reduction and principal component analysis, k-means clustering, decision trees, support vector machines, artificial neural networks and hidden Markov models.
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.
Mode of Delivery	Face to Face

COURSE LEARNING OUTCOMES

1	Lists basic pattern recognition concepts
2	Explains statistical methods
3	Explains linear methods
4	Explains nonlinear methods

COURSE'S CONTRIBUTION TO PROGRAM

	PO 01	PO 02	PO 03	PO 04	PO 05	PO 06	PO 07	PO 08	PO 09	PO 10	PO 11	PO 12
LO 001	4	4	2	2		5					4
LO 002	4	4	2	2		5					4
LO 003	4	4	2	2		5					4
LO 004	4	4	2	2		5					4
Sub Total	16	16	8	8		20					16
Contribution	4	4	2	2	0	5	0	0	0	0	4	0

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION

Activities	Quantity	Duration (Hour)	Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)	14	3	42
Hours for off-the-classroom study (Pre-study, practice)	14	2	28
Mid-terms	1	30	30
Final examination	1	30	30
Total Work Load ECTS Credit of the Course			130 5

COURSE DETAILS

Select Year

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester
CENG 420	MACHINE LEARNING & PATTERN RECOGNITION	3 + 0	1	Turkish	2023-2024 Spring

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Asts. Prof. Dr. ALPER UĞUR	augur@pau.edu.tr		MUH A0435	%70

Goals		Purpose of this course is to teach fundamental concepts of machine learning and pattern recognition.
Content		Introduction to machine learning, supervised learning, regression, model order and generalization properties, Bayes decision theory, maximum likelihood method, distance functions, multivariable models and regression, dimensionality reduction and principal component analysis, k-means clustering, decision trees, support vector machines, artificial neural networks and hidden Markov models.

Topics

Weeks	Topics
1	Introduction and Basics
2	Decision Trees, Random forest algorithms
3	Weka app, Regression
4	Classification Rule based naive bayes
5	Classification svm
6	Clustering kmeans
7	Knime software
8	ANN
9	Tensorflow
10	Tensorflow
11	Image Classification
12	Image Classification
13	Project demos
14	Project demos

Materials

Materials are not specified.

Resources

Resources	Resources Language
Weka, https://www.cs.waikato.ac.nz/ml/weka/	English
Tensorflow, https://www.tensorflow.org/	English
Singh, H. (2019) Practical machine learning and image processing : for facial recognition, object detection, and pattern recognition using python. Berkeley, California: Apress. doi: 10.1007/978-1-4842-4149-3.	English
Rebala, G., Ravi, A. and Churiwala, S. (2019), An Introduction to Machine Learning, Springer	English

Course Assessment

L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes

Prospective Student