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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
EKOM 544DEEP LEARNING METHODS AND APPLICATIONS3 + 02nd Semester7,5

COURSE DESCRIPTION
Course Level Master's Degree
Course Type Elective
Course Objective To provide the students with theoretical and mostly applied study of; deep learning, artificial neural networks and relevant advanced topics in machine learning and data science. To establish in-depth knowledge of deep hierarchical models and learning mechanisms in computers, deep vs. shallow architectures, convolutional networks, LSTM and their applications to pattern recognition, speech recognition and natural language processing.
Course Content Introduction to Artificial Neural Networks, Basics of Artificial Neural Networks, Deep Recurrent Networks, LSTM, GRU and sequence learning. Simple Recurrent NN: Elman and Jordan networks. Standard Recurrent NN, Long/Short Term Memory.
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1Implement and develop solutions for problems in companies or institutions by the aid of deep learning and convolutional neural networks.
2Gain knowledge to effectively develop and implement convolutional and LSTM neural network models for data science applications.
3Gain knowledge to effectively develop and implement deep learning models by using Python-based platforms such as TensorFlow, Keras, PyTorch, and so on.
4Be able to describe and use different methodologies, procedures and techniques in deep learning.
5Develop or implement research projects in the area of deep learning and convolutional neural networks.

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10PO 11PO 12
LO 001            
LO 002            
LO 003            
LO 004            
LO 005            
Sub Total            
Contribution000000000000

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14570
Hours for off-the-classroom study (Pre-study, practice)14570
Assignments11010
Mid-terms12020
Final examination12525
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