Pamukkale University
University is the guide to life
Welcome to PAU;
Prospective Student
Our Students
Our Staff
TR
Information Package & Course Catalogue
Home Page
About University
Name And Address
Acedemic Authorities
General Discription
Academic Calendar
General Admission Requirements
Recognition of Prior Learning
General Registration Procedures
ECTS Credit Allocation
Academic Guidance
Information For Students
Cost Of Living
Accommodation
Meals
Medical Facilities
Facilities for Special Needs Students
Insurance
Financial Support for Students
Student Affairs
Learning Facilities
International Programs
Language Courses
Internships
Sports Facilities and Leisure Activities
Student Associations
Practical Information for Mobile Students
Degree Programmes
SECOND CYCLE - MASTER'S DEGREE
THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES
BIOMEDICAL ENGINEERING DEPARTMENT
1507 Biomedical Engineering
Course Information
Course Learning Outcomes
Course's Contribution To Program
ECTS Workload
Course Details
Print
COURSE INFORMATION
Course Code
Course Title
L+P Hour
Semester
ECTS
BMM 534
MACHINE LEARNING AND APPLİCATİONS
3 + 0
1st 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
PO 01
PO 02
PO 03
PO 04
PO 05
PO 06
PO 07
PO 08
PO 09
PO 10
PO 11
PO 12
PO 13
PO 14
LO 001
LO 002
LO 003
Sub Total
Contribution
0
0
0
0
0
0
0
0
0
0
0
0
0
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
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
All Years
2023-2024 Spring
Course Term
No
Instructors
Details
2023-2024 Spring
1
EŞREF BOĞAR
Print
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
{1}
##LOC[OK]##
{1}
##LOC[OK]##
##LOC[Cancel]##
{1}
##LOC[OK]##
##LOC[Cancel]##
Home Page
About University
Name And Address
Acedemic Authorities
General Discription
Academic Calendar
General Admission Requirements
Recognition of Prior Learning
General Registration Procedures
ECTS Credit Allocation
Academic Guidance
Information For Students
Cost Of Living
Accommodation
Meals
Medical Facilities
Facilities for Special Needs Students
Insurance
Financial Support for Students
Student Affairs
Learning Facilities
International Programs
Language Courses
Internships
Sports Facilities and Leisure Activities
Student Associations
Practical Information for Mobile Students
Degree Programmes