COURSE UNIT TITLE

: ARTIFICIAL INTELLIGENCE

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
MKT 4222 ARTIFICIAL INTELLIGENCE ELECTIVE 2 2 0 3

Offered By

Mechatronics

Level of Course Unit

Short Cycle Programmes (Associate's Degree)

Course Coordinator

ASSISTANT PROFESSOR TANER AKKAN

Offered to

Mechatronics
Mechatronics (Evening)

Course Objective

The students know and apply artifical intelligence methods and control systems that suit to human thinking and decision systems. The students know artificial intelligence algorithms, build fuzzy logic and neural network systems.

Learning Outcomes of the Course Unit

1   be able to define artificial intelligence concept.
2   be able to understand artifiical intelligence and Turing machine.
3   be able to define basic brain functioning and neuron systems.
4   be able to set up neural network systems according to neural network types and operations.
5   be able to apply fuzzy logic systems
6   be able to make software of neural network control applications.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Definitions of artificial intelligence.
2 Comparison between classical control and artifical intelligence control applications.
3 Turing machine and its operation.
4 Neuron and nerve transmissions.
5 Operation of the human brain and types of artificial neural networks.
6 Artificial neural networks and simulations in computers.
7 Artificial neural networks and simulations in computers.
8 Mid-term exam
9 Fuzzy logic concepts.
10 Set up fuzzy logic systems, fuzzificiation, de-fuzzification,
11 Fuzzy logic systems simulations.
12 Artificial intelligence control systems and today applications.
13 Artificial intelligence control systems in industry and industrial applications.
14 Artificial intelligence robots.
15 Artifical intelligence applications

Recomended or Required Reading

1-Yapay Zeka (Problemler - Yöntemler - Algoritmalar), Doç. Dr. Vasif Vagifoğlu Nabiyev, Seçkin Yayıncılık, Mayıs 2005
2- Artificial Intelligence Course Notes, Derleyen Yard.Doç.Dr. Taner AKKAN
3- Artificial Intelligence: A Modern Approach, (Third edition) by Stuart Russell and Peter Norvig, Aima, 2010.
4- Internet

Planned Learning Activities and Teaching Methods

1. Lectures
2. Applications

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 FN Final
3 FCG FINAL COURSE GRADE VZ*0.40 + FN* 0.60
4 RST RESIT
5 FCGR FINAL COURSE GRADE (RESIT) VZ*0.40 + BUT* 0.60


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Learning outcomes are measured with mid-term and final exams and the level of reaching to learning outcomes for students are tracked.

Language of Instruction

Turkish

Course Policies and Rules

70% attendance is mandatory for the course.

Contact Details for the Lecturer(s)

E-mail : taner.akkan@deu.edu.tr Phone : 0232 3012585

Office Hours

will be announced at the beginning of the semester.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 2 28
Tutorials 14 2 28
Preparations before/after weekly lectures 14 1 14
Preparation for midterm exam 1 6 6
Preparation for final exam 1 7 7
Final 1 1 1
Midterm 1 1 1
TOTAL WORKLOAD (hours) 85

Contribution of Learning Outcomes to Programme Outcomes

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CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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