Automotive Mechatronics and Intelligent Vehicles (with thesis)
Master	TR-NQF-HE: Level 7	QF-EHEA: Second Cycle	EQF-LLL: Level 7

General course introduction information

Course Code:

AUTO560

Course Name:

Intelligent Sensors and Control for Autonomous Systems

Course Semester:

Fall

Course Credits:

Theoretical	Practical	Credit	ECTS
		3	10

Language of instruction:

Course Requisites:

Does the Course Require Work Experience?:

Type of course:

Department Elective

Course Level:

Master

TR-NQF-HE:7. Master`s Degree

QF-EHEA:Second Cycle

EQF-LLL:7. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Dr.Öğr.Üyesi MAHSA MIKAEILI

Course Lecturer(s):

Course Assistants:

Course Objective and Content

Course Objectives:

The course aims to present the fundamentals and techniques of Artificial Intelligence.

Course Content:

In the first part of the course, an overview of intelligent agents and agent architectures is presented. Then, basic search techniques for problem-solving and planning are introduced. Competitive search methods and the fundamental principles of game theory are addressed. Knowledge representation and logical formulation are explained using propositional logic and first-order logic. The topic of planning in partially observable environments is also discussed.

In the second part of the course, the fundamental concepts of probability theory for artificial intelligence applications are summarized. Then, supervised and unsupervised learning algorithms are examined. The concept of deep learning is briefly addressed. Applications of artificial intelligence in areas such as computer vision, robotics, and natural language processing are discussed. Finally, the societal impacts and ethical considerations of artificial intelligence are covered.

The course is conducted through theoretical lectures, practical exercises, algorithm development, case analysis, and discussions.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Gains knowledge about the fundamental principles of artificial intelligence.
2 - Skills
Cognitive - Practical
1) Can define the state space of a problem and develop an algorithm for the problem.
2) Can compare and evaluate the most common models for knowledge representation and planning.
3) Can implement some basic algorithms for supervised and unsupervised learning.
4) Develops problem-solving skills on various artificial intelligence problems and can implement related applications.
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
Competence to Work Independently and Take Responsibility

Lesson Plan

Week	Subject	Related Preparation
1)	A Review of AI Concepts Rational Agents	Chapter 1, Stuart Russell, Peter Norvig, “Artificial Intelligence: A Modern Approach” (3rd Edition), Prentice Hall, ISBN-10: 0-13-604259-7, 2010.
2)	Solving Problems by searching - Search algorithms (Uninformed and Informed)	Chapter 3 Stuart Russell, Peter Norvig, “Artificial Intelligence: A Modern Approach” (3rd Edition), Prentice Hall, ISBN-10: 0-13-604259-7, 2010.
3)	Solving Problems by searching - Constraint Satisfaction Problems	Chapter 4 Stuart Russell, Peter Norvig, “Artificial Intelligence: A Modern Approach” (3rd Edition), Prentice Hall, ISBN-10: 0-13-604259-7, 2010.
4)	Games - Adversarial Search, Game theory	Chapter 5 Stuart Russell, Peter Norvig, “Artificial Intelligence: A Modern Approach” (3rd Edition), Prentice Hall, ISBN-10: 0-13-604259-7, 2010.
5)	Logical agents - Propositional logic, First Order Logic and inference	Chapter 7, Stuart Russell, Peter Norvig, “Artificial Intelligence: A Modern Approach” (3rd Edition), Prentice Hall, ISBN-10: 0-13-604259-7, 2010.
6)	Planning	Chapter 10,11, Stuart Russell, Peter Norvig, “Artificial Intelligence: A Modern Approach” (3rd Edition), Prentice Hall, ISBN-10: 0-13-604259-7, 2010.
7)	Probabilistic Reasoning - Basic probability concepts, Bayesian inference	Chapter 14, Stuart Russell, Peter Norvig, “Artificial Intelligence: A Modern Approach” (3rd Edition), Prentice Hall, ISBN-10: 0-13-604259-7, 2010.
8)	Probabilistic Reasoning - Naive Bayes models, Bayesian networks	Chapter 14, Stuart Russell, Peter Norvig, “Artificial Intelligence: A Modern Approach” (3rd Edition), Prentice Hall, ISBN-10: 0-13-604259-7, 2010.
8)	Probabilistic Reasoning - Naive Bayes models, Bayesian networks	Chapter 14, Stuart Russell, Peter Norvig, “Artificial Intelligence: A Modern Approach” (3rd Edition), Prentice Hall, ISBN-10: 0-13-604259-7, 2010.
8)	Probabilistic Reasoning - Naive Bayes models, Bayesian networks	Chapter 14, Stuart Russell, Peter Norvig, “Artificial Intelligence: A Modern Approach” (3rd Edition), Prentice Hall, ISBN-10: 0-13-604259-7, 2010.
9)	Machine Learning - Supervised vs. unsupervised learning, Decision trees, Nearest neighbor classifiers, Support Vector Machines	Chapter 18,Stuart Russell, Peter Norvig, “Artificial Intelligence: A Modern Approach” (3rd Edition), Prentice Hall, ISBN-10: 0-13-604259-7, 2010.
10)	Neural Networks	Chapter 18.1, Stuart Russell, Peter Norvig, “Artificial Intelligence: A Modern Approach” (3rd Edition), Prentice Hall, ISBN-10: 0-13-604259-7, 2010
11)	Deep Learning - Convolutional Neural Networks	Chapter 6, Deep Learning, An MIT Press book Ian Goodfellow and Yoshua Bengio and Aaron Courville
12)	Deep Learning	Chapter 12, Deep Learning, An MIT Press book Ian Goodfellow and Yoshua Bengio and Aaron Courville
13)	Reinforcement Learning - Markov decision processes, Q-learning	Chapter 21, Stuart Russell, Peter Norvig, “Artificial Intelligence: A Modern Approach” (3rd Edition), Prentice Hall, ISBN-10: 0-13-604259-7, 2010.
14)	AI, Ethics and Society

Sources

Course Notes / Textbooks:	Stuart Russell, Peter Norvig, “Artificial Intelligence: A Modern Approach” (3rd Edition), Prentice Hall, ISBN-10: 0-13-604259-7, 2010. Deep Learning, An MIT Press book Ian Goodfellow and Yoshua Bengio and Aaron Courville
References:	Stuart Russell, Peter Norvig, “Artificial Intelligence: A Modern Approach” (3rd Edition), Prentice Hall, ISBN-10: 0-13-604259-7, 2010. Deep Learning, An MIT Press book Ian Goodfellow and Yoshua Bengio and Aaron Courville

Course-Program Learning Outcome Relationship

Learning Outcomes	1	2	3	4	5
Program Outcomes
1) Sufficient knowledge in mathematics, science and engineering related to their branches; and the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems.
2) The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose.
3) The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. (Realistic constraints and conditions include such issues as economy, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues, according to the nature of design.)
4) Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively.
5) Ability to design experiments, conduct experiments, collect data, analyze and interpret results to examine engineering problems or discipline-specific research topics.
6) The ability to work effectively in disciplinary and multidisciplinary teams; individual work skill.
7) Effective communication skills in Turkish oral and written communication; at least one foreign language knowledge; ability to write effective reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8) Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal.
9) Conform to ethical principles, and standards of professional and ethical responsibility; be informed about the standards used in engineering applications.
10) Awareness of applications in business, such as project management, risk management and change management; awareness of entrepreneurship, and innovation; information about sustainable development.
11) Information about the universal and social health, environmental and safety effects of engineering applications and the ways in which contemporary problems are reflected in the engineering field; awareness of the legal consequences of engineering solutions.
12) Knowledge on advanced calculus, including differential equations applicable to automotive engineering; familiarity with statistics and linear algebra; knowledge on chemistry, calculus-based physics, dynamics, structural mechanics, structure and properties of materials, fluid dynamics, heat transfer, manufacturing processes, electronics and control, design of vehicle elements, vehicle dynamics, vehicle power train systems, automotive related regulations and vehicle validation/verification tests; ability to integrate and apply this knowledge to solve multidisciplinary automotive problems; ability to apply theoretical, experimental and simulation methods and, computer aided design techniques in the field of automotive engineering; ability to work in the field of vehicle design and manufacturing.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Sufficient knowledge in mathematics, science and engineering related to their branches; and the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems.
2)	The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose.
3)	The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. (Realistic constraints and conditions include such issues as economy, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues, according to the nature of design.)
4)	Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively.
5)	Ability to design experiments, conduct experiments, collect data, analyze and interpret results to examine engineering problems or discipline-specific research topics.
6)	The ability to work effectively in disciplinary and multidisciplinary teams; individual work skill.
7)	Effective communication skills in Turkish oral and written communication; at least one foreign language knowledge; ability to write effective reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8)	Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal.
9)	Conform to ethical principles, and standards of professional and ethical responsibility; be informed about the standards used in engineering applications.
10)	Awareness of applications in business, such as project management, risk management and change management; awareness of entrepreneurship, and innovation; information about sustainable development.
11)	Information about the universal and social health, environmental and safety effects of engineering applications and the ways in which contemporary problems are reflected in the engineering field; awareness of the legal consequences of engineering solutions.
12)	Knowledge on advanced calculus, including differential equations applicable to automotive engineering; familiarity with statistics and linear algebra; knowledge on chemistry, calculus-based physics, dynamics, structural mechanics, structure and properties of materials, fluid dynamics, heat transfer, manufacturing processes, electronics and control, design of vehicle elements, vehicle dynamics, vehicle power train systems, automotive related regulations and vehicle validation/verification tests; ability to integrate and apply this knowledge to solve multidisciplinary automotive problems; ability to apply theoretical, experimental and simulation methods and, computer aided design techniques in the field of automotive engineering; ability to work in the field of vehicle design and manufacturing.

Learning Activity and Teaching Methods

Expression
Brainstorming/ Six tihnking hats
Lesson
Homework
Report Writing
Q&A / Discussion

Assessment & Grading Methods and Criteria

Written Exam (Open-ended questions, multiple choice, true-false, matching, fill in the blanks, sequencing)
Homework
Application
Individual Project
Presentation
Reporting

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	5	% 10
Project	1	% 25
Midterms	1	% 25
Final	1	% 40
total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
total		% 100

Workload and ECTS Credit Grading

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Project	1	20	20
Homework Assignments	5	3	15
Midterms	1	3	3
Final	1	2	2
Total Workload			82