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
1627 BIOMEDICAL ENGINEERING PHD
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 677
DEEP LEARNING APPLICATIONS IN MEDICAL DATA
3 + 0
1st Semester
7,5
COURSE DESCRIPTION
Course Level
Master's Degree
Course Type
Elective
Course Objective
The aim of this course is to learn the basic concepts and model architectures of deep learning, which is an artificial intelligence method, and to perform applications in medical data with MATLAB and Python programs.
Course Content
Basic concepts, Introduction to Machine Learning, Artificial Neural Networks, Deep learning concepts, Hyperparameters, Optimization and Regularization, Convolutional Neural Networks, Deep learning architectures, Classification and prediction in medical data, Use of deep learning in medical data, Matlab and Python deep learning libraries, Matlab and medical data classification, prediction, and object detection using Python deep learning libraries
Prerequisites
No the prerequisite of lesson.
Corequisite
No the corequisite of lesson.
Mode of Delivery
Face to Face
COURSE LEARNING OUTCOMES
1
To learn the fundamentals of deep learning
2
To be able to describe the basic model architectures of deep learning
3
To be able to use deep learning methods in medical data
4
To be able to develop software that can analyze medical data using deep learning libraries in MATLAB and Python languages.
COURSE'S CONTRIBUTION TO PROGRAM
Data not found.
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
This course is not available in selected semester.
Print
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