1	Course Title:	ARTIFICIAL INTELLIGENCE
2	Course Code:	BMB3015
3	Type of Course:	Optional
4	Level of Course:	First Cycle
5	Year of Study:	3
6	Semester:	5
7	ECTS Credits Allocated:	5
8	Theoretical (hour/week):	3
9	Practice (hour/week) :	0
10	Laboratory (hour/week) :	0
11	Prerequisites:	None
12	Recommended optional programme components:	None
13	Language:	Turkish
14	Mode of Delivery:	Face to face
15	Course Coordinator:	Dr. Ögr. Üyesi CEYDA NUR ÖZTÜRK
16	Course Lecturers:
17	Contactinformation of the Course Coordinator:	ceydanur@uludag.edu.tr
18	Website:
19	Objective of the Course:	To introduce the methods and tools that are used for making computer systems equipped with the abilities of problem solving, inference, learning, communication, perception, and action in various environments
20	Contribution of the Course to Professional Development	Engineering Science: 70%, Engineering Design: 30%

21

Learning Outcomes:

1 Being able to design artificial intelligence systems that are appropriate for working in different contexts; 2 Being able to solve a problem state-space of which is defined using different search methods ; 3 Being able to program inferential problems using artificial intelligence languages; 4 Being able to select and implement appropriate learning methods for the problems; 5 Being informed about the basic concepts and issues of communication, perception, and action ;

22	Course Content:

Week	Theoretical	Practical
1	Introduction, intelligent agents
2	Problem solving by searching, uninformed search algorithms
3	Informed search algorithms
4	Local search algorithms
5	Adversarial search
6	Logical inference, first-order logic
7	Prolog programming
8	Knowledge representation and semantic networks
9	Learning from observations, decision trees
10	Uncertainty, statistical inference, Bayesian learning
11	Artificial neural networks, backpropagation algorithm
12	Communication, formal grammars, syntactic and semantic analyses
13	Perception, image formation and image processing
14	Action, robot localization, mapping and planning

23	Textbooks, References and/or Other Materials:	1. Russell, S., and Norvig, P., 2016. Artificial Intelligence: A Modern Approach, 3rd Edition, Pearson Education, ISBN-10: 0136042597 ISBN-13: 978-1292153964. 2. Zhang, A., Lipton, Z. C., Li, M., and Smola, A. J., 2022. Dive into deep learning. arXiv preprint DOI: https://doi.org/10.48550/arXiv.2106.11342. 3. Ekman, M., 2021. Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, NLP, and Transformers Using TensorFlow, Addison-Wesley Professional, ISBN-10: 0137470355 ISBN-13: 978-0137470358.
24	Assesment

TERM LEARNING ACTIVITIES	NUMBER	PERCENT
Midterm Exam	1	10
Quiz	0	0
Homeworks, Performances	3	30
Final Exam	1	60
Total	5	100
Contribution of Term (Year) Learning Activities to Success Grade		40
Contribution of Final Exam to Success Grade		60
Total		100
Measurement and Evaluation Techniques Used in the Course	Programming and study assignments, written exams
Information

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ECTS / WORK LOAD TABLE

Activites	NUMBER	TIME [Hour]	Total WorkLoad [Hour]
Theoretical	14	3	42
Practicals/Labs	0	0	0
Self Study and Preparation	14	2	28
Homeworks, Performances	3	0	0
Projects	0	0	0
Field Studies	0	0	0
Midtermexams	1	40	40
Others	0	0	0
Final Exams	1	40	40
Total WorkLoad			150
Total workload/ 30 hr			5
ECTS Credit of the Course			5

26	CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS
PQ1 PQ2 PQ3 PQ4 PQ5 PQ6 PQ7 PQ8 PQ9 PQ10 PQ11 PQ12 LO1 5 4 4 3 2 3 2 1 2 2 0 2 LO2 5 5 5 5 3 4 3 1 3 3 0 0 LO3 5 4 5 5 3 4 3 1 3 3 0 0 LO4 5 5 5 5 3 4 3 1 3 3 0 0 LO5 4 4 3 3 1 2 2 1 2 2 0 1

LO: Learning Objectives

PQ: Program Qualifications

Contribution Level:

1 Very Low

2 Low

3 Medium

4 High

5 Very High