| Course Objectives: |
Artificial Intelligence explains various problem solving techniques and introduces basic machine learning techniques such as supervised and unsupervised learning. Apart from that, it aims to give a solid understanding of basic machine learning problems. In addition, the course introduces current machine learning methods such as decision trees, linear regression, k-nearest neighbor, Bayesian classifiers, neural networks, logistic regression and classifier combinations. |
| Course Content: |
Within the scope of this course, first of all, introduction to Artificial Intelligence, the history of artificial intelligence, its basics; Application areas of artificial intelligence, followed by intelligent agents (agents), logical agents, problem solving by searching, games and puzzles, Heuristic search from various search algorithms, Local search, Hostile search, then Genetic algorithms, Machine Learning Clustering Algorithms, K- Nearest Neighboring Algorithm, Decision Trees, Fuzzy Logic topics will be covered.
This course employs the project-based learning approach. In this respect aside from the conventional content the course has a project-based learning component. The project based- learning component aims realising one or more projects designed for learning purposes involving the development of certain intermediary and final deliverables in a step-by-step mannerby the students individually or in project teams. The evaluation of the project-based learning component involves grading the project deliverables and the project works by the instructor and/or a jury. |
Course Learning Outcomes (CLOs) are those describing the knowledge, skills and competencies that students are expected to achieve upon successful completion of the course. In this context, Course Learning Outcomes defined for this course unit are as follows:
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| Knowledge
(Described as Theoritical and/or Factual Knowledge.)
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1) Understand the fundamental concepts of knowledge based reasoning.
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2) Know the fundamental concepts of agents and applications of agent theory to different domains.
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| Skills
(Describe as Cognitive and/or Practical Skills.)
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1) Formulate an efficient problem space for a problem expressed in English by expressing that problem space in terms of states, operators, and initial state, and a description of goal state.
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2) Select an appropriate search algorithm (i.e. brute-force, heuristic) for a problem, implement it, and characterize its time and space complexities.
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3) Know (be able to explain the differences between and implement simple algorithms of) three main styles of learning: supervised, reinforcement and unsupervised.
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4) Apply concepts of artificial intelligence to different domains including games and puzzles, expert systems, planning, learning, vision, robotics and natural language understanding.
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5) Program small scale artificial intelligence applications using Prolog.
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| Competences
(Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)
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| Week |
Subject |
Materials Sharing * |
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Related Preparation |
Further Study |
| 1) |
Introduction to artificial intelligence
The history and foundations of artificial intelligence, intelligent agents |
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| 2) |
Smart Factors (Agent), Logical Factors, Determination of Projects |
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| 3) |
Solving problems by searching, beyond classical research |
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| 4) |
Heuristic Search |
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| 5) |
Local Search |
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| 6) |
Adversarial Search |
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| 7) |
Fuzzy Logic |
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| 8) |
Midterm Exam |
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| 9) |
Project 1 st review |
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| 10) |
Introduction to Machine Learning |
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| 11) |
Supervised Learning: Classification Algorithms, Linear Regression, Decision Trees |
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| 12) |
Supervised Learning: k-Nearest Neighbor Algorithm (KNN), Support Vector Machines (SVM), Artificial Neural Networks (ANN) |
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| 13) |
Unsupervised Learning: Clustering Algorithms, K-Means Algorithm, Hierarchical Clustering, |
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| 14) |
AI Exercises and Project Presentation |
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Programme Learning Outcomes |
Contribution Level (from 1 to 5) |
| 1) |
Explain the fundamental concepts, historical development, and theoretical framework of graphic design. |
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| 2) |
Define typography, color theory, and composition principles in visual communication design. |
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| 3) |
Evaluate the social, cultural, and ethical aspects of graphic design to develop an interdisciplinary perspective. |
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| 4) |
Develop original and innovative design solutions using creative problem-solving methods. |
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| 5) |
Apply visual hierarchy, perception psychology, and user experience (UX) principles to design for international markets. |
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| 6) |
Effectively use digital tools and design software to produce professional graphic design work. |
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| 7) |
Take responsibility in international graphic design projects individually or within a team to develop creative solutions. |
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| 8) |
Manage graphic design projects and plan processes while applying a professional work discipline. |
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| 9) |
Continuously improve by following global innovations, technologies, and methodologies in graphic design. |
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| 10) |
Adopt intercultural design principles to create visual solutions for global audiences. |
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| 11) |
Develop design solutions that are culturally sensitive, ethically appropriate, and sustainable. |
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| 12) |
Work independently or participate in teamwork within graphic design processes. |
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| WORKLOAD OF TEACHING & LEARNING ACTIVITIES |
| Teaching & Learning Activities |
# of Activities per semester |
Duration (hour) |
Total Workload |
| Course |
14 |
2 |
28 |
| Laboratory |
0 |
0 |
0 |
| Application |
14 |
1 |
14 |
| Special Course Internship (Work Placement) |
0 |
0 |
0 |
| Field Work |
0 |
0 |
0 |
| Study Hours Out of Class |
14 |
4 |
56 |
| Presentations / Seminar |
4 |
1 |
4 |
| Project |
1 |
26 |
26 |
| Homework Assignments |
0 |
0 |
0 |
| Total Workload of Teaching & Learning Activities |
- |
- |
128 |
| WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES |
| Assesment & Evaluation Activities |
# of Activities per semester |
Duration (hour) |
Total Workload |
| Quizzes |
0 |
0 |
0 |
| Midterms |
1 |
12 |
12 |
| Semester Final Exam |
1 |
12 |
12 |
| Total Workload of Assesment & Evaluation Activities |
- |
- |
24 |
| TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities) |
152 |
| ECTS CREDITS OF THE COURSE (Total Workload/25.5 h) |
6 |