What you will learn?
Intoduction to AI
Problem Solving and Search Algorithms
Knowledge Rpresentation
Introduction to Machine Learning
Neural Networks
Natural Language Processing
About this course
Week 1: Introduction to AI
- Overview of AI: History and Applications
- The Turing Test and Intelligence
Week 2: Problem Solving and Search Algorithms
- State Space and Problem Representation
- Search Strategies: Depth-First, Breadth-First, A* Algorithm
Week 3: Knowledge Representation
- Logic and Propositional Calculus
- Ontologies and Semantic Networks
Week 4: Introduction to Machine Learning
- Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning
- Key Algorithms: Decision Trees, k-Nearest Neighbors
Week 5: Neural Networks
- Basics of Neural Networks and Deep Learning
- Activation Functions and Backpropagation
Week 6: Natural Language Processing
- Text Processing and Sentiment Analysis
- Language Models and Chatbots
Week 7: Computer Vision
- Image Processing Basics
- Convolutional Neural Networks (CNNs)
Week 8: Robotics and AI
- Introduction to Robotics
- AI in Robotics: Sensors and Navigation
Week 9: AI Ethics and Society
- Ethical Issues in AI
- Bias in AI Systems
Week 10: Project Presentations
- Student presentations on AI projects
Assessment Methods
- Homework Assignments (40%): Weekly assignments based on lecture content and practical applications.
- Midterm Exam (20%): An exam covering material from weeks 1-5.
- Final Project (30%): A group project that applies AI concepts to a real-world problem.
- Class Participation (10%): Active participation in discussions and activities.
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