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.
Lessons
Lab
This lab introduces the Google Colab environment, focusing on mounting Google Drive for persistent storage, managing Python packages with pip, and utilizing GPUs for efficient tensor computations. Students also explore essential data structures and interactive widgets to prepare for upcoming assignments in symbolic AI and deep learning.
This lab introduces Symbolic AI through the classic SHRDLU "Blocks World," where students learn to represent world states, implement symbolic physics, and use regex for natural language parsing. The assignment progresses from direct execution to automated planning, teaching students how to use recursive logic to resolve physical constraints and clear obstacles in a simulated environment.
This lab introduces the fundamentals of building a Gomoku AI by covering 2D grid representation, nested loops for board traversal, and direction vectors for win detection. Students will also learn to implement the Minimax algorithm with recursive thinking, heuristic scoring, and backtracking to enable the AI to evaluate future game states efficiently.
This lab focuses on optimizing Gomoku AI by implementing strategic heuristics, such as positional heatmaps, and performance enhancements like spatial locality and move ordering. These techniques reduce the search space and improve the efficiency of the Minimax algorithm, allowing the AI to search deeper and make more intelligent decisions.
This lab provides a comprehensive guide on structuring a technical assignment report for COMP5511, covering essential sections like the abstract, introduction, background theory, and methodology. Students will learn how to effectively document their work using Markdown, apply academic writing standards, and clearly explain complex AI concepts such as symbolic planning and adversarial search.
This lab introduces the transition from symbolic AI to machine learning by using the MNIST dataset to train a Support Vector Machine (SVM) classifier. Students learn to process image data using PyTorch tensors, normalize pixel values, and apply Scikit-Learn’s standardized workflow to flatten and classify handwritten digits.
This lab introduces Convolutional Neural Networks (CNNs) as a solution to the limitations of classic machine learning by preserving spatial information in images through filters, activation functions, and pooling. Students learn to implement these architectures in PyTorch using nn.Module and understand the iterative training process of forward passes, loss calculation, and backpropagation.
This lab explores the limitations of traditional machine learning models like SVMs when handling complex, high-dimensional data and demonstrates how deep CNNs overcome these challenges through GPU acceleration and batch processing. Students will learn to scale their models using PyTorch to efficiently process larger datasets like CIFAR-10, highlighting the necessity of parallel computing in modern deep learning.
This lab introduces the transition from computer vision to Natural Language Processing by exploring how language models generate text through iterative token prediction. Students will gain hands-on experience using the Hugging Face library to load, tokenize, and run instruction-tuned mini-LLMs like Qwen-0.6B on local hardware.
This lab introduces fine-tuning as an efficient way to adapt a pre-trained Qwen model to a specific persona using a small, conversationally formatted dataset. Students will learn to prepare data, apply chat templates, and use tokenization techniques to transform a standard assistant into an emoji-based communicator.
This lab explores how to overcome the limitations of Large Language Models by building AI Agents that combine a "Brain" (the LLM) with "Hands" (Python tools and APIs) to interact with the real world. Students learn to implement an iterative "Think-Act-Observe" loop, using system instructions and regex parsing to enable the AI to fetch and process live data.
This lab introduces the ReAct (Reasoning + Acting) pattern, which improves AI performance by forcing models to "think out loud" before executing tasks. Students will learn to overcome the limitations of stateless LLMs by implementing a scratchpad memory and an iterative loop that integrates external Wikipedia tool data as observations.
Mock
This mock exam provides a self-paced review and practice opportunity for the PolyU COMP5511 course to help students prepare for the upcoming assessment. It serves as a tool to test your understanding of key course concepts and evaluate your readiness for the final exam.