ARTIFICIAL INTELLIGENCE
- Course
- CMPE415 - ARTIFICIAL INTELLIGENCE
- Department
- Computer Engineering - English - Undergraduate
- Course Type
- Course
- Status
- Required
- Language
- English
- Credit
- 3
- ECTS
- 7
- T+P+L
- 3 + 0 + 1
- Course Coordinator(s)
- Assoc. Prof. Dr. Kamil YURTKAN
- Prerequisite
- Keywords
Course Description
This course teaches artificial intelligence from an intelligent systems perspective which includes the methods (tools) to build systems that can plan, learn, reason and interact intelligently with their environment. The course introduces the key components of the artificial intelligence (AI), the agent-based AI architecture, artificial intelligence techniques to solve problems for a particular domain, appropriate search methods in achieving desired goals, and knowledge representation using various techniques. The topics are as follows: intelligent agents, problem solving, uninformed search strategies, informed search strategies, knowledge representation, logical inference, propositional logic, first-order logic. The artificial intelligence methods studied are experimented using a programming language and the students are expected to complete a project related to an artificial intelligence algorithm with its software implementation.
ARTIFICIAL INTELLIGENCE
Evaluation Tools (Active Term)
No evaluation items have been defined.
Course outcomes
- 01 gain an understanding of the key components of the artificial intelligence (AI)
- 02 gain an understanding of the agent-based AI architecture
- 03 apply artificial intelligence techniques and classifiers in solving problems of a particular domain
- 04 implement basic learning algorithms in machine learning
- 05 represent knowledge using various techniques
Course Syllabus
| Week | Topic |
|---|---|
| Week 1 | Introduction to AI and Problem Solving |
| Week 2 | Intelligent Agents |
| Week 3 | Intelligent Agents |
| Week 4 | Introduction to Machine Learning |
| Week 5 | Introduction to Neural Networks - Perceptron Learning |
| Week 6 | Introduction to Neural Networks - Feed Forward Neural Networks |
| Week 7 | Introduction to Neural Networks - Hopfield Network |
| Week 8 | Uninformed Search Strategies - Breadth First Search, Uniform Cost Search, Depth First Search |
| Week 9 | Uninformed Search Strategies - Breadth First Search, Uniform Cost Search, Depth First Search |
| Week 10 | Midterm Week |
| Week 11 | Midterm Week |
| Week 12 | Informed Search Strategies - Greedy Search , A* Search |
| Week 13 | Informed Search Strategies - Greedy Search , A* Search |
| Week 14 | Representation and Logic - Propositional Logic |
| Week 15 | Representation and Logic - First Order Logic |
Reference Books & Course Materials
- 01 S. Russell, P. Norvig, "Artificial Intelligence: A Modern Approach", Prentice-Hall, 2003.
- 02 Simon Haykin, Neural Networks: A Comprehensive Foundation 2nd Ed. Prentice Hall PTR Upper Saddle River, NJ, USA ©1998 ISBN:0132733501
Learning Outcomes
No learning outcomes have been defined.
Program Outcomes
- P01 Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in complex engineering problems.
- P02 Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modelling methods for this purpose.
- P03 Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose.
- P04 Ability to devise, select, and use modern techniques and tools needed for analysing and solving complex problems encountered in engineering practice; ability to employ information technologies effectively
- P05 Ability to design and conduct experiments, gather data, analyse and interpret results for investigating complex engineering problems or discipline specific research questions.
- P06 Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
- P07 Ability to communicate effectively in Turkish, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions.
- P08 Recognition of the need for lifelong learning ; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.
- P09 Consciousness to behave according to ethical principles and professional and ethical responsibility; knowledge on standards used in engineering practice.
- P10 Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development.
- P11 Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions.
Po-Lo Matrix
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