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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS BIL 4112 ARTIFICIAL INTELLIGENCE ELECTIVE 3 0 0 5

Offered By

Computer Science

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSISTANT PROFESSOR KADRIYE FILIZ BALBAL

Offered to

Computer Science

Course Objective

The main purpose of this course is to provide the most fundamental knowledge related to the AI to the students so that they can understand what the AI is.

Learning Outcomes of the Course Unit

1   Have a good understanding of the description of the AI. 2   Have a good understanding of the integration of AI with real problems. 3   Have a good understanding of the techniques used in AI 4   Have ability to make comparison between techniques used in AI and their solution ways. 5   Have a good understanding of the theory of AI techniques

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 AI: Definitions and basic terms 2 Data and Learning 3 Logistic Regression 4 Artificial Neural Networks (ANN)-1 5 Artificial Neural Networks (ANN)-2 6 Convolutional Neural Networks (CNN)-1 7 Convolutional Neural Networks (CNN)-2 8 Applications 9 Recurrent Neural Networks (RNN)-1 10 Recurrent Neural Networks (RNN)-2 11 Genetic algorithms 12 Presentations 13 Presentations 14 Presentations

Recomended or Required Reading

Textbook(s): Russell, S.J. And Norvig, P., Artificial Intelligence : A Modern Approach, Third Edition, Prentice-Hall, 2009. Supplementary Book(s): Goodfellow, Ian & Bengio, Yoshua & Courville, Aaron (2016), MIT Press, 781 pages. Tom M. Mitchell, Machine Learning, McGraw Hill, 1997.

Planned Learning Activities and Teaching Methods

Lecture format, built around the textbook readings with numerous examples chosen to illustrate theoretical concepts.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 ASG ASSIGNMENT 2 FIN FINAL EXAM 3 FCGR FINAL COURSE GRADE (RESIT) ASG * 0.60 + FIN * 0.40 4 RST RESIT 5 FCGR FINAL COURSE GRADE (RESIT) ASG * 0.60 + RST * 0.40 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

kadriyefiliz.balbal@deu.edu.tr

Office Hours

will be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 13 3 39 Preparations before/after weekly lectures 12 3 36 Preparing assignment 1 15 15 Preparing report 1 10 10 Preparation for final exam 1 30 30 Final 1 2 2 TOTAL WORKLOAD (hours) 132

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 PO.11 PO.12 PO.13 LO.1 4 5 5 3 LO.2 4 4 5 LO.3 5 LO.4 5 4 3 5 LO.5 5 5 5 4