PolyU | Artificial Intelligence Concepts
This comprehensive course provides a rigorous yet accessible introduction to Artificial Intelligence, designed for postgraduate students and professionals. Bridging the gap between historical foundations and cutting-edge innovations, the curriculum progresses from symbolic AI and search algorithms to modern Deep Learning and Generative AI. Students will explore essential topics such as knowledge representation, probabilistic reasoning, and classical machine learning before diving deep into neural networks, Transformers, and Large Language Models (LLMs). Emphasizing both theory and practice, the course utilizes Python and industry-standard frameworks like PyTorch to implement algorithms, interact with modern APIs, and address critical issues in AI ethics and safety.
Course Overview
Content Summary
This comprehensive course provides a rigorous yet accessible introduction to Artificial Intelligence (AI), designed for postgraduate students and professionals. Bridging the gap between historical foundations and cutting-edge innovations, the curriculum progresses from symbolic AI and search algorithms to modern Deep Learning and Generative AI. Students will explore essential topics such as knowledge representation, probabilistic reasoning, and classical machine learning before diving deep into neural networks, Transformers, and Large Language Models (LLMs). Emphasizing both theory and practice, the course utilizes Python and industry-standard frameworks like PyTorch to implement algorithms, interact with modern APIs, and address critical issues in AI ethics and safety.
Bridging the gap between classical Symbolic AI and modern Generative AI, this course equips students with the theoretical depth and practical Python skills to architect, implement, and ethically evaluate intelligent systems.
Learning Objectives
- Master AI Foundations: Analyze and implement fundamental problem-solving paradigms, including heuristic search, logic-based reasoning, and probabilistic modeling.
- Implement Deep Learning Architectures: Design and train advanced neural networks using PyTorch, ranging from Multi-layer Perceptrons to Convolutional Neural Networks and Transformers.
- Deploy Generative AI Solutions: Engineer applications utilizing Large Language Models (LLMs), employing techniques such as fine-tuning, prompt engineering, and Retrieval-Augmented Generation (RAG).
- Ensure Ethical AI Development: Critically evaluate AI systems for bias, safety, and alignment, applying strategies for explainability and robustness in real-world scenarios.