Information Package / Course Catalogue
Artificial Intelligence Applications With Python
Course Code: MME545
Course Type: Area Elective
Couse Group: Second Cycle (Master's Degree)
Education Language: English
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 8
Objectives of the Course

The course aims to equip learners with the skills to implement AI solutions and stay abreast of advanced AI trends. The main objective of this course is to familiarize students with the fundamental concepts, theories, and applications of artificial intelligence. Students will gain insight into the various subfields of AI, such as machine learning, natural language processing, computer vision, and robotics.

Course Content

This AI with Python course provides a comprehensive introduction to the world of Artificial Intelligence, emphasizing practical applications using the Python programming language. Participants will delve into fundamental AI concepts, explore machine learning techniques, delve into deep learning and neural networks, engage in real-world AI project development, and critically examine the ethical considerations surrounding AI technologies.

Name of Lecturer(s)
Prof. Pınar DEMİRCİOĞLU
Learning Outcomes
1.Students will have a clear understanding of the fundamental concepts and terminology of Artificial Intelligence
2.Students will be able to discuss and comprehend AI-related topics
3.Students will be proficient in writing Python programs
4.Students will be proficient in understanding syntax, and applying programming constructs
5.Students will have a solid foundation for further programming endeavours
Recommended or Required Reading
1.S. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach, Prentice Hall
2.M. Tim Jones, “Artificial Intelligence: A Systems Approach (Computer Science)”, Jones and Bartlett Publishers, Inc.; 1st Edition, 2008
3.Nils J. Nilsson, “The Quest for Artificial Intelligence”, Cambridge University Press, 2009
4.Python GUI programming Cookbook -Burkahard A Meier, Packt Publication, 2nd Edition.
5.Barry, P. (2016). Head first Python: A brain-friendly guide. “ O’Reilly Media, Inc.”. Lutz, M. (2013). Learning python: Powerful object-oriented programming. “O’Reilly Media, Inc.”
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to Python and AI Concepts
Week 2 - Theoretical
Machine Learning with Python
Week 3 - Theoretical
Deep Learning and Neural Networks
Week 4 - Theoretical
AI Project Development and Ethics
Week 5 - Theoretical
Advanced AI Topics and Trends
Week 6 - Theoretical
Natural Language Processing (NLP)
Week 7 - Theoretical
Computer Vision
Week 8 - Intermediate Exam
Computer Vision, Midterm Exam
Week 9 - Theoretical
Reinforced Learning
Week 10 - Theoretical
Time Series Analysis with AI
Week 11 - Theoretical
AI in Healthcare
Week 12 - Theoretical
AI in Finance
Week 13 - Theoretical
AI in Autonomous Systems
Week 14 - Theoretical
Explainable AI and Model Interpretability
Week 15 - Final Exam
Final Exam
Week 16 - Final Exam
Final Exam
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%30
Final Examination1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory143498
Assignment70535
Individual Work73342
Midterm Examination19211
Final Examination112214
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
OÇ-1
3
5
5
5
5
5
3
5
OÇ-2
3
4
4
4
5
5
4
3
4
4
OÇ-3
3
5
5
4
5
4
4
5
OÇ-4
3
5
4
4
4
4
4
4
OÇ-5
3
5
5
5
3
4
3
4
Adnan Menderes University - Information Package / Course Catalogue
2026