General Information
- University: Tishk International University
- Department: Computer Engineering Dept
- My Status: Visiting Lecturer
- Level: BSc
- Year: 2018
Course Description
This course provides a comprehensive introduction to the fundamental concepts, techniques, and applications of Artificial Intelligence (AI). Students will explore the theoretical foundations of AI while gaining practical experience in implementing AI algorithms and systems. The course covers essential topics including search algorithms, knowledge representation, machine learning, and intelligent agent design.
The curriculum emphasizes both classical AI approaches and modern techniques, enabling students to understand the evolution of AI and its current applications. Through hands-on projects and programming assignments, students will develop the skills necessary to design and implement intelligent systems that can solve complex problems in various domains.
This course prepares students for advanced studies in AI, machine learning, robotics, and related fields, while also providing practical knowledge applicable to software development and system design.
Prerequisites
- Programming Fundamentals (Python preferred)
- Data Structures and Algorithms
- Linear Algebra and Calculus
- Probability and Statistics
- Logic and Discrete Mathematics
Course Objectives
Upon completion of this course, students will be able to:
- Understand the fundamental principles and philosophical foundations of artificial intelligence.
- Implement and analyze various search algorithms for problem-solving.
- Design and implement knowledge representation systems using logic and semantic networks.
- Apply machine learning techniques for pattern recognition and prediction.
- Develop intelligent agents capable of autonomous decision-making.
- Evaluate AI systems and understand their limitations and ethical implications.
- Apply AI concepts to real-world problems in various domains.
Course Outline
Week 1: Introduction to Artificial Intelligence
- Definition and scope of artificial intelligence
- History and evolution of AI
- AI applications in modern computing
- Philosophical foundations and Turing test
- Current trends and future directions in AI
- Lab: Setting up AI development environment
Week 2: Problem Solving and Search
- Problem-solving agent architecture
- Uninformed search strategies (BFS, DFS, UCS)
- Informed search algorithms (A*, greedy search)
- Adversarial search and game playing
- Lab: Implementing search algorithms
Week 3: Advanced Search and Game Playing
- Constraint satisfaction problems
- Game tree search and minimax algorithm
- Alpha-beta pruning and optimization
- Game playing applications
- Lab: Game playing algorithm implementation
Week 4: Knowledge Representation and Reasoning
- Propositional logic and first-order logic
- Knowledge representation schemes
- Semantic networks and frames
- Rule-based systems and expert systems
- Lab: Knowledge representation systems
Week 5: Logical Reasoning and Expert Systems
- Logical reasoning and inference engines
- Backward chaining and forward chaining
- Expert system architecture
- Knowledge acquisition and representation
- Lab: Expert system development
Week 6: Machine Learning Fundamentals
- Supervised learning concepts
- Classification and regression algorithms
- Unsupervised learning techniques
- Reinforcement learning basics
- Lab: Basic machine learning algorithms
Week 7: Midterm Exam and Review
- Midterm Exam: Covers weeks 1-6 material
- Review of AI fundamentals and search
- Problem-solving practice
- Lab: Exam review and practice problems
Week 8: Neural Networks and Deep Learning
- Artificial neural network architecture
- Backpropagation algorithm
- Deep learning concepts and applications
- Convolutional neural networks (CNNs)
- Lab: Neural network implementation
Week 9: Natural Language Processing
- Language understanding and generation
- Text processing and analysis
- Information extraction and retrieval
- Machine translation systems
- Lab: NLP applications and text processing
Week 10: Computer Vision
- Image processing fundamentals
- Feature extraction and recognition
- Object detection and tracking
- Image classification and segmentation
- Lab: Computer vision applications
Week 11: Intelligent Agents and Robotics
- Agent architectures and types
- Multi-agent systems
- Robotics fundamentals
- Autonomous navigation and control
- Lab: Agent-based simulation
Week 12: Uncertainty and Probabilistic Reasoning
- Probability theory in AI
- Bayesian networks and inference
- Hidden Markov models
- Decision theory and utility
- Lab: Probabilistic reasoning systems
Week 13: AI Ethics and Social Impact
- Ethical considerations in AI development
- Bias and fairness in AI systems
- Privacy and security concerns
- AI safety and control
- Lab: AI ethics case studies
Week 14: Final Exam Preparation and Review
- Comprehensive review of all course material
- Practice problems and sample questions
- Final Exam: Theoretical component
- Lab: Final exam practice and preparation
Week 15: Final Project and Course Wrap-up
- Final Exam: Practical AI project
- Course evaluation and feedback
- Future learning paths and advanced topics
- Lab: Final project presentation and evaluation
Textbooks
- [Recommended] “Artificial Intelligence: A Modern Approach” by Stuart Russell and Peter Norvig
- [Optional] “Pattern Recognition and Machine Learning” by Christopher M. Bishop
Assessment
- Programming Assignments (25%)
- AI Project Development (25%)
- Midterm Exam (20%)
- Final Exam (30%)