Information Package / Course Catalogue - Adnan Menderes University

General Information Unit Staff Academic Information Programme Outcomes Course Structure Diagram Contact Information

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Advanced Robotics & Artificial Intelligence

Course Code	: MME638
Course Type	: Area Elective
Couse Group	: Third Cycle (Doctorate Degree)
Education Language	: English
Work Placement	: N/A

Theory	: 3
Prt.	: 0
Credit	: 3
Lab	: 0
ECTS	: 8

Objectives of the Course

This course introduces students to the broad field of robotics predominately from an artificial intelligence perspective. , Students will deepen their knowledge in up to three of the six fields of AI, robot navigation, deep learning, data science, machine learning, and computer vision.

Course Content

The course starts by answering general questions such as what a robot is, their origins, types, and applications. It then introduces the key approaches to robot control and explores the pros and cons of each. The course covers the advanced topics of robot learning and bio-inspired robots.

Name of Lecturer(s)

Learning Outcomes

1.	Students can describe what a robot is, their origin, types, and applications
2.	Students can describe, compare, and apply robot control stategies
3.	Students can apply robot software tools
4.	Students can apply bio-inspired solutions and machine learning appropriately in robotics
5.	Students can formulate and reflect on robot solutions to real-world problems

Recommended or Required Reading

1.	Artificial Intelligence: A Modern Approach Author: Stuart J. Russell and Peter Norvig Publisher: Pearson Edition: 4th Edition ISBN: 0-13-461099-7
2.	Russell, S. & Norvig, P. Artificial Intelligence: A Modern Approach, Pearson, 2020

Weekly Detailed Course Contents

Week 1 - Theoretical

Fields of AI and robotics

Week 2 - Theoretical

Fundamental concepts in AI and robotics

Week 3 - Theoretical

Fundamental principles in AI and robotics

Week 4 - Theoretical

Fundamental theories in AI and robotics

Week 5 - Theoretical

Machine learning, data science

Week 6 - Theoretical

Deep learning, natural language processing

Week 7 - Theoretical

Computer vision

Week 8 - Intermediate Exam

Computer vision, Midterm Exam

Week 9 - Theoretical

Reinforcement learning

Week 10 - Theoretical

Control systems

Week 11 - Theoretical

Analyzing a given problem

Week 12 - Theoretical

Finding solutions to a given problem

Week 13 - Theoretical

Solutions and approaches to problems based on a concept or a combination of concepts presented

Week 14 - Theoretical

State-of-the-art AI and robotics tools and platforms

Week 15 - Final Exam

Final Exam

Week 16 - Final Exam

Final Exam

Assessment Methods and Criteria

Type of Assessment	Count	Percent
Midterm Examination	1	%30
Final Examination	1	%70

Workload Calculation

Activities	Count	Preparation	Time	Total Work Load (hours)
Lecture - Theory	14	3	4	98
Assignment	7	0	5	35
Individual Work	7	3	3	42
Midterm Examination	1	9	2	11
Final Examination	1	12	2	14
TOTAL WORKLOAD (hours)				200

Contribution of Learning Outcomes to Programme Outcomes

	PÇ-1	PÇ-2	PÇ-3	PÇ-4	PÇ-5	PÇ-6	PÇ-7	PÇ-8	PÇ-9	PÇ-10	PÇ-11	PÇ-12	PÇ-13	PÇ-14
OÇ-1	4	4	4	4		4	3		4			4
OÇ-2	4	4	4	4	3	3	3		4			4
OÇ-3	4	4	4	4	4	5	3		4			4
OÇ-4	4	4	4	4	4	5	3		4			4
OÇ-5	4	5	5	5	5	5	5		5			5

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