PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
CENG 528	MACHINE LEARNING	3 + 0	2nd Semester	7,5

COURSE DESCRIPTION

Course Level	Master's Degree
Course Type	Elective
Course Objective	The main objective of this course is to teach students the concept of machine learning and different learning methods. At the end of the course, the students will gain skills for selecting and applying an appropriate learning methods for real-life problems and analyzing the performance of the method in terms of error and complexity.
Course Content	Supervised learning, Bayesian decision theory, parametric methods, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, hidden Markov models, support vector machines, unsupervised learning, reinforcement learning
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.
Mode of Delivery	Face to Face

COURSE LEARNING OUTCOMES

1	Distinguishes the differences between machine learning methods.
2	Knows the idea of how to select the most appropriate parameters when implementing a machine learning method.
3	Applies the analysis of applicable machine learning methods on a given data by coding it on the computer.

COURSE'S CONTRIBUTION TO PROGRAM

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	5	70
Assignments	5	8	40
Mid-terms	1	15	15
Final examination	1	28	28
Total Work Load ECTS Credit of the Course			195 7,5

COURSE DETAILS

Select Year

	Course Term	No	Instructors
Details	2023-2024 Fall	1	SERDAR İPLİKÇİ

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester
CENG 528	MACHINE LEARNING	3 + 0	1	Turkish	2023-2024 Fall

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Prof. Dr. SERDAR İPLİKÇİ	iplikci@pau.edu.tr		MUH A0203 MUH A0257	%

Goals		The main objective of this course is to teach students the concept of machine learning and different learning methods. At the end of the course, the students will gain skills for selecting and applying an appropriate learning methods for real-life problems and analyzing the performance of the method in terms of error and complexity.
Content		Supervised learning, Bayesian decision theory, parametric methods, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, hidden Markov models, support vector machines, unsupervised learning, reinforcement learning

Topics

Materials

Materials are not specified.

Resources

Course Assessment

Assesment Methods	Percentage (%)	Assesment Methods Title
Final Exam	50	Final Exam
Midterm Exam	50	Midterm Exam

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

Prospective Student