PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
BMM 534	MACHINE LEARNING AND APPLİCATİONS	3 + 0	2nd Semester	7,5

COURSE DESCRIPTION

Course Level	Master's Degree
Course Type	Elective
Course Objective	This course aims at providing a theoretical basis for machine learning and its use with examples of biomedical applications.
Course Content	Introduction to machine learning, multi-variable models and regression, supervised learning, bayesian learning, model selection, artificial neural networks, nearest neighbor, support vector machines, decision trees, 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	Knows fundamental concepts about machine learning
2	Knows machine learning structures
3	Can solve real-world problem by using machine learning methods

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
Mid-terms	1	56	56
Final examination	1	56	56
Special Study Module (Student)	1	41	41
Total Work Load ECTS Credit of the Course			195 7,5

COURSE DETAILS

Select Year

	Course Term	No	Instructors
Details	2023-2024 Spring	1	EŞREF BOĞAR

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester
BMM 534	MACHINE LEARNING AND APPLİCATİONS	3 + 0	1	Turkish	2023-2024 Spring

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Asts. Prof. Dr. EŞREF BOĞAR	ebogar@pau.edu.tr		TEK A0101	%

Goals		This course aims at providing a theoretical basis for machine learning and its use with examples of biomedical applications.
Content		Introduction to machine learning, multi-variable models and regression, supervised learning, bayesian learning, model selection, artificial neural networks, nearest neighbor, support vector machines, decision trees, 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