Dokuz Eylül University Information Package / Courses Catalog

Description of Individual Course Units

Course Unit Code	Course Unit Title	Type Of Course	D	U	L	ECTS
CSC 5020	DEEP LEARNING AND CONVOLUTIONAL NEURAL NETWORKS	ELECTIVE	3	0	0	8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSISTANT PROFESSOR KADRIYE FILIZ BALBAL

Offered to

Ph.D. in Computer Science (English)
Computer Science

Course Objective

To provide the students with a comprehensive theoretical and applied study of; deep learning, convolutional neural networks and relevant advanced topics in machine learning and artificial intelligence.

Learning Outcomes of the Course Unit

1	Implement and develop solutions for problems in companies or institutions by the aid of deep learning and convolutional neural networks.
2	Gain knowledge to effectively design and implement convolutional neural network models for natural language processing and text mining.
3	Gain knowledge to effectively design and implement deep learning models for pattern recognition and machine vision.
4	Plan, manage and use different methodologies, procedures and techniques in deep learning and convolutional neural networks.
5	Develop or implement research projects in the area of deep learning and convolutional neural networks.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week	Subject	Description
1	Introduction to Deep Learning.
2	Introduction to Artificial Neural Networks.
3	Fundamental Topics and Methods in Artificial Neural Networks.
4	Introduction to Pattern Recognition and Artificial Vision/Perception.
5	Fundamental Concepts in Convolutional Neural Networks.
6	Applications and Technologies of Convolutional Neural Networks.
7	Applications and Technologies of Deep Recurrent Neural Networks.
8	Long Short-Term Memory (LSTM)
9	Recurrent Neural Networks (RNN)
10	Comparative Analysis of Deep Learning Models. Complex Problems and Current Issues Related to Convolutional Neural Networks and Deep Learning.
11	Presentations
12	Presentations
13	Presentations
14	General discussion and review of the topics covered throughout the term.

Recomended or Required Reading

Y. Bengio, I. Goodfellow and A. Courville, Deep Learning , MIT Press, 2016.
L. Deng and D. Yu, Deep Learning: Methods and Applications , Foundations and Trends in Signal Processing, Now Publishers, 2014.

Supplementary Book(s):
C. Bishop, Pattern Recognition and Machine Learning , Springer, 2006.
K. P. Murphy, Machine learning: a probabilistic perspective , MIT press, 2012.

Planned Learning Activities and Teaching Methods

The course is taught in a lecture, class presentation and discussion format. Besides the taught lecture, group presentations are to be prepared by the groups assigned and presented in a discussion session. In some weeks of the course, results of the homework given previously are discussed.

Assessment Methods

SORTING NUMBER	SHORT CODE	LONG CODE	FORMULA
1	PRS	PRESENTATION
2	ASG	ASSIGNMENT
3	FCG	FINAL COURSE GRADE	PRS * 0.50 + ASG * 0.50

Further Notes About Assessment Methods

None.

Assessment Criteria

None.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

mete.eminagaoglu@deu.edu.tr

Office Hours

Will be announced.

Work Placement(s)

None

Workload Calculation

Activities	Number	Time (hours)	Total Work Load (hours)
Lectures	14	3	42
Preparations before/after weekly lectures	13	4	52
Preparation for final exam	1	24	24
Preparing assignments	2	30	60
Preparing presentations	2	14	28
Final	1	2	2
TOTAL WORKLOAD (hours)			208

Contribution of Learning Outcomes to Programme Outcomes

PO/LO	PO.1	PO.4	PO.5	PO.6
LO.1	4	4	4	4
LO.2	4	4	4	4
LO.3	4	4	4	4
LO.4	4	4	4	4
LO.5	4	4	4	4

COURSE UNIT TITLE

DEGREE PROGRAMMES

Description of Individual Course Units

Offered By

Level of Course Unit

Course Coordinator

Offered to

Course Objective

Learning Outcomes of the Course Unit

Mode of Delivery

Prerequisites and Co-requisites

Recomended Optional Programme Components

Course Contents

Recomended or Required Reading

Planned Learning Activities and Teaching Methods

Assessment Methods

Further Notes About Assessment Methods

Assessment Criteria

Language of Instruction

Course Policies and Rules

Contact Details for the Lecturer(s)

Office Hours

Work Placement(s)

Workload Calculation

Contribution of Learning Outcomes to Programme Outcomes

CONTACT INFORMATION