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 Apply data science principles and techniques to challenges in real life situations, and effectively communicate their solutions.
- P02 Identify and implement data analysis methodologies based on theoretical ideas, ethical code, and in-depth knowledge of the underlying data.
- P03 Analyze the guiding concepts and assessment procedures for information analysis in real-life applications.
- P04 Design and apply relevant data analysis models to find obscure solutions to business-related problems.
- P05 Utilize modern computing techniques to handle real-world problems characterized by massive amounts of data, such as parallel and distributed computing and machine learning.
- P06 Configure and administer the software tools required to efficiently produce usable information from any size of structured and unstructured datasets.
- P07 Administer or manage data science tools and techniques to organize and complete projects aimed at gaining useful insight from complex data.
- P08 Think critically and imaginatively, conceiving real-world issues from several angles, and work well in a variety of teams to solve issues cooperatively.
- P09 Be able to effectively integrate data‐based solutions into the user environment and help non-technical professionals in exploring, visualizing, and using these solutions
- P10 Understand their obligations under professional and ethical standards in relation to matters like data ownership and citation, data security and sensitivity and the privacy implications of data analysis.
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