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COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
BMM 677	DEEP LEARNING APPLICATIONS IN MEDICAL DATA	3 + 0	2nd Semester	7,5

COURSE DESCRIPTION

Course Level	Doctorate 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

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

Prospective Student