Artificial Intelligence

This course provides an introduction to Artificial Intelligence (AI) and covers key concepts, algorithms, and applications. Students will gain practical experience in areas such as search algorithms, machine learning, natural language processing, and computer vision.

Content:

•  Introduction to Artificial Intelligence
• Overview of Artificial Intelligence, its history, and applications
• Differentiating between weak AI and strong AI
• Ethical considerations and societal impacts of AI
•  Problem Solving and Search Algorithms
• Problem formulation and representation in AI
• Uninformed search algorithms (BFS, DFS)
• Heuristic search algorithms (A*, greedy search)
•  Knowledge Representation and Reasoning
• Predicate logic and propositional logic
• Inference and resolution in logic
• Frames, semantic networks, and ontologies
• Machine Learning Basics
• Introduction to supervised learning, unsupervised learning, and reinforcement learning
• Linear regression and classification algorithms
• Model evaluation and validation techniques
• Natural Language Processing (NLP)
• Processing and understanding text data
• Part-of-speech tagging, named entity recognition
• Sentiment analysis and text summarization
   
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•  Computer Vision and Image Processing
• Introduction to computer vision tasks (object detection, image segmentation)
• Image preprocessing, feature extraction, and image classification
• Applications of computer vision in real-world scenarios
•  Neural Networks and Deep Learning
• Basics of artificial neural networks
• Training neural networks using backpropagation
• Convolutional Neural Networks (CNNs) for image processing
• Final Projects and Capstone
• Applying AI concepts to real-world scenarios
• Conducting a complete AI project
• Final project presentation and evaluation
• Assessment:
• Class participation and engagement
• Homework assignments, coding exercises, and practical assessments
• Mid-term and final projects
• Final project presentation and evaluation