Course Details

INTRODUCTION TO AI

COMP460

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Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS Credits
7COMP460YAPAY ZEKAYA GİRİŞ3+0+035

Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program COMPUTER ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course • Introducing the fundamental concepts and principles of Artificial Intelligence and its subfields.
• Empowering students with the knowledge to utilize AI tools and platforms without requiring programming skills.
• Exploring applications of AI across various disciplines and its implications for society.
• Developing critical and ethical thinking skills regarding the deployment and consequences of AI technologies.
Course Content This introductory course offers non-engineering students a comprehensive understanding of Artificial Intelligence (AI) without the need for programming skills. Participants will explore foundational concepts, methodologies, and tools used in AI, including Machine Learning, Deep Learning, and Large Language Models (LLMs). Through hands-on activities using user-friendly tools and prompt engineering techniques, students will gain insights into real-world AI applications. The course emphasizes developing critical thinking and ethical perspectives on AI's role in society.
Course Methods and Techniques This course will be conducted by synchronous online live sessions via platforms like Zoom or Microsoft Teams. All lectures, discussions, and practical activities are delivered in real-time to ensure active student participation and immediate feedback from the instructor. Theoretical concepts, such as AI fundamentals, machine learning, and large language models, are introduced through live online lectures incorporating multimedia presentations, real-world case studies, and interactive Q&A sessions. Students are encouraged to engage actively during live classes through polls, quizzes, and live discussions. Practical skills are developed through live demonstrations and hands-on exercises using AI tools like MATLAB Classification & Regression Learner and Experiment Manager. Students follow the instructor's guidance step-by-step in real time, with opportunities to ask questions and receive personalized support. Screen sharing is used to facilitate technical learning. The course emphasizes exercises, assignments and a final exam about machine learning and prompt engineering, where students develop AI-based solutions for recent challenges. Collaborative group work is facilitated through breakout rooms, allowing students to engage in peer-to-peer learning and teamwork. Continuous assessment is carried out through live assignments with feedback provided during live sessions. This fully synchronous approach fosters real-time engagement, active learning, and continuous interaction between students and the instructor.
Prerequisites and co-requisities None
Course Coordinator Associate Prof.Dr. Zafer Aydın - zafer.aydin@agu.edu.tr
Name of Lecturers Associate Prof.Dr. RIFAT KURBAN
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources • https://www.mathworks.com/videos/classify-data-using-the-classification-learner-app-106171.html
Online video lectures, courses and tutorials about MATLAB, machine learning, and prompt engineering
• https://www.mathworks.com/videos/classify-data-using-the-classification-learner-app-106171.html
• https://www.mathworks.com/videos/forecast-electrical-load-using-the-regression-learner-app-1536231842528.html
• https://www.mathworks.com/videos/series/introduction-to-machine-learning.html
• https://www.mathworks.com/solutions/machine-learning.html
• https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/
• https://www.youtube.com/watch?v=_ZvnD73m40o
• https://www.coursera.org/learn/prompt-engineering
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Course Category
Mathematics and Basic Sciences %10
Engineering %20
Engineering Design %20
Social Sciences %10
Education %10
Science %20
Health %10
Field %0

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Ödev 5 % 60
Final examination 1 % 40
Total
6
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Yazılı Sınav 1 14 14
Ev Ödevi 5 8 40
Senkron Ders 14 3 42
Ders dışı çalışma 14 3 42
Total Work Load   Number of ECTS Credits 5 138

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Explain the fundamental principles of AI, including key concepts such as machine learning, deep learning and large language models.
2 Empowering students with the knowledge to utilize AI tools and platforms without requiring programming skills.
3 Exploring applications of AI across various disciplines and its implications for society.
4 Developing critical and ethical thinking skills regarding the deployment and consequences of AI technologies.


Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to Artificial Intelligence Definition, history, significance, applications, ethics, and societal impact of AI - -
2 Machine Learning Concepts Types of ML: Supervised, unsupervised, and reinforcement learning; practical examples and tools - -
3 Machine Learning Concepts Model training, testing, performance metrics, overfitting, underfitting - -
4 Machine Learning Concepts Hyper-parameter optimization, cross-validation, data leakage, decision trees, and random forests - -
5 Data Preparation and Feature Engineering Loading, cleaning, imputation, transformation, encoding, and data augmentation - -
6 Data Analysis with MATLAB Classification & Regression Learner App Loading and visualizing data - -
7 Data Analysis with MATLAB Classification & Regression Learner App Feature selection algorithms, principal component analysis - -
8 Data Analysis with MATLAB Classification & Regression Learner App Building and testing classification and regression models, hyper-parameter optimization - -
9 Data Analysis with MATLAB Classification & Regression Learner App Interpreting results and making informed decisions - -
10 Deep Learning Basics Neural networks and their significance - -
11 Data Analysis with MATLAB Experiment Manager App Training and testing deep learning models - -
12 Large Language Models (LLMs) Overview of LLMs, how they work, and examples like GPT - -
13 Prompt Engineering Crafting effective prompts for AI tools and use cases for interdisciplinary problem-solving - -
14 Interdisciplinary Applications of AI, AI in healthcare, finance, education, and future trends in AI - -


Contribution of Learning Outcomes to Programme Outcomes
P1
All 5
C1 5
C2 5
C3 5
C4 5

Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant


https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=77659&lang=en
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