| Overall Educational Objective |
To gain a deep understanding of artificial intelligence fundamentals, explore various AI technologies and algorithms, and their applications in real-world scenarios. Students will learn to design, implement, and optimize AI solutions to solve complex problems and innovate in their respective fields. |
| Course Description |
The ITC503 Artificial Intelligence course provides an in-depth look into the world of artificial intelligence, covering key concepts, methodologies, and applications of AI technologies. Students will explore machine learning algorithms, neural networks, natural language processing, and robotics, understanding how these technologies can be applied to solve complex problems in business, healthcare, finance, and more. The course aims to equip students with the skills necessary to implement and optimize AI solutions, fostering innovation and advancing their careers in the rapidly evolving AI industry. |
| Course Description in Turkish |
ITC503 Yapay Zeka dersi, yapay zeka dünyasına derinlemesine bir bakış sunar, AI teknolojilerinin temel kavramlarını, metodolojilerini ve uygulamalarını kapsar. Öğrenciler, makine öğrenimi algoritmalarını, sinir ağlarını, doğal dil işlemeyi ve robot teknolojilerini keşfedecek, bu teknolojilerin iş, sağlık, finans ve daha birçok alanda karmaşık problemleri çözmek için nasıl uygulanabileceğini anlayacaklardır. Ders, öğrencilere AI çözümlerini uygulama ve optimize etme becerileri kazandırmayı, yenilikçiliği teşvik etmeyi ve hızla gelişen AI endüstrisinde kariyerlerini ilerletmeyi amaçlamaktadır. |
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Program Outcomes and Competences |
Level |
Assessed by |
| 1) |
An ability to develop and deepen one's knowledge in the field of mechatronics and robotics engineering at the level of expertise based on acquired undergraduate level qualifications. |
N |
|
| 2) |
An ability to acquire scientific and practical knowledge in mechatronics and robotics. |
N |
|
| 3) |
A comprehensive knowledge about analysis and modeling methods in mechatronics and their limitations. |
N |
|
| 4) |
An ability to design and apply analytical, modeling and experimental based research by analyzing and interpreting complex situations encountered in the design process. |
N |
|
| 5) |
An ability to transmit the process and results of the work of mechatronics and robotics systems systematically and clearly in written and oral form in national and international environments. |
N |
|
| 6) |
An ability to recognize social, scientific and ethical values in the stages of designing and realizing mechatronics and robotic systems and in all professional activities. |
N |
|
| 7) |
An ability to follow new and developing practices in the profession and to apply them in their work. |
N |
|
| 8) |
An ability to take leadership in multi-disciplinary teams, taking responsibility in the design and analysis of mechatronics and robotic systems in complex situations. |
N |
|
| 9) |
An ability to communicate verbally and in writing in English at least at the level of B2 of European Language Portfolio. |
N |
|
| 10) |
An understanding of the social and environmental aspects of mechatronics and robotics applications. |
N |
|
| Week |
Subject |
| 1) |
● Introduction to Artificial Intelligence
● Overview of AI, its history, evolution, and the distinction between AI, machine learning, and deep learning.
|
| 2) |
● Foundations of Machine Learning
● An introduction to the basic concepts of machine learning, including supervised, unsupervised, and reinforcement learning.
|
| 3) |
● Neural Networks and Deep Learning
● Exploring the structure and functionality of neural networks; an introduction to deep learning frameworks.
|
| 4) |
● Natural Language Processing (NLP)
● Basics of NLP, including text processing, sentiment analysis, and language models.
|
| 5) |
● Computer Vision
● Introduction to computer vision; understanding image recognition, object detection, and image generation techniques.
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| 6) |
● Robotics in AI
● Overview of robotics; how AI is applied in robotics for navigation, decision-making, and performing tasks.
|
| 7) |
● Ethical Considerations in AI
● Discussion on the ethical implications of AI, including bias, privacy, and future societal impacts.
|
| 8) |
● AI in Healthcare
● Exploring applications of AI in healthcare, from diagnostics to personalized medicine and patient care.
|
| 9) |
● AI in Business and Finance
● Examination of AI applications in business analytics, financial modeling, and algorithmic trading.
|
| 10) |
● Reinforcement Learning
● Introduction to reinforcement learning, including key algorithms and their applications in AI.
|
| 11) |
● AI Tools and Libraries
● Hands-on sessions with popular AI tools and libraries (e.g., TensorFlow, PyTorch).
|
| 12) |
● Project Work – Proposal and Design
● Students propose and begin design work on their AI projects, applying concepts learned in class.
|
| 13) |
● Project Work – Implementation
● Continued project work, focusing on implementing AI models and analyzing results.
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| 14) |
● Review and Future of AI
● Course review, discussion on the future trends in AI, and its potential impact on various sectors.
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| 15) |
Final Examination Period |
| 16) |
Final Examination Period |
Information Package / Course Catalogue