Information Package / Course Catalogue
Artificial Intelligence and Robotics
Course Code: CSE446
Course Type: Area Elective
Couse Group: First Cycle (Bachelor's Degree)
Education Language: English
Work Placement: N/A
Theory: 2
Prt.: 2
Credit: 3
Lab: 0
ECTS: 6
Objectives of the Course

This course aims to provide a comprehensive introduction to the fundamental concepts and techniques in artificial intelligence and robotics. It focuses on intelligent agent design, machine learning principles, and robotic systems development.

Course Content

• Introduction to Artificial Intelligence (AI) and Robotics • Problem-solving and search strategies • Knowledge representation and reasoning • Supervised and unsupervised learning • Neural networks and deep learning • Computer vision and image processing • Robotics basics: sensors, actuators, and controllers • Robotic motion planning and kinematics • Human-robot interaction and ethical issues

Name of Lecturer(s)
Lec. Mahmut SİNECEN
Learning Outcomes
1.Define key concepts of artificial intelligence and robotics.
2.Apply basic AI algorithms for problem-solving and learning.
3.Design and train machine learning models
4.Understand sensor and actuator integration in robotic systems
5.Plan and simulate robot movements.
6.Evaluate ethical implications of AI and robotic applications
Recommended or Required Reading
1.Stuart Russell & Peter Norvig, "Artificial Intelligence: A Modern Approach"
2.Sebastian Thrun vd., "Probabilistic Robotics"
3.Roland Siegwart vd., "Introduction to Autonomous Mobile Robots"
4.Lecture Notes and Online Tutorials
Weekly Detailed Course Contents
Week 1 - Theoretical & Practice
Introduction to AI and Robotics
Week 2 - Theoretical & Practice
Intelligent Agents and Environments
Week 3 - Theoretical & Practice
Problem Solving by Search
Week 4 - Theoretical & Practice
Adversarial Search and Game Playing
Week 5 - Theoretical & Practice
Knowledge and Reasoning
Week 6 - Theoretical & Practice
Machine Learning Overview
Week 7 - Theoretical & Practice
Supervised Learning Algorithms
Week 8 - Theoretical & Practice
Unsupervised Learning & Clustering
Week 9 - Theoretical & Practice
Neural Networks and Deep Learning
Week 10 - Theoretical & Practice
Robotics Basics and Kinematics
Week 11 - Theoretical & Practice
Sensor and Actuator Integration
Week 12 - Theoretical & Practice
Motion Planning and Control
Week 13 - Theoretical & Practice
Human-Robot Interaction & Ethics
Week 14 - Theoretical & Practice
Project Presentations and Review
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%30
Final Examination1%40
Assignment2%10
Project1%20
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory142370
Lecture - Practice141128
Assignment23210
Project115520
Midterm Examination110111
Final Examination110111
TOTAL WORKLOAD (hours)150
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
OÇ-1
5
4
4
4
4
3
3
3
3
2
4
OÇ-2
5
5
5
4
5
4
4
3
3
3
4
OÇ-3
5
5
4
5
5
4
5
4
4
3
5
OÇ-4
4
4
4
4
5
5
4
5
4
4
3
OÇ-5
4
5
5
4
5
5
4
4
3
3
5
OÇ-6
3
4
4
4
4
3
4
4
4
5
4
Adnan Menderes University - Information Package / Course Catalogue
2026