Artificial Intelligence in Aviation Industry

课程简介

/ Indicative Syllabus Fundamental of Artificial Intelligence (AI): Basic concepts and trends of AI; AI algorithm design; deep learning; large language models. Data Analytics: Data processing; data mining; data visualization; principle component analysis; data-driven optimization. Supervised learning: Least squares and nearest neighbours; statistical decision theory; Linear methods for regression; Linear discriminant analysis; Classifications; Logistic regression; Support-vector machine. Unsupervised learning: Clustering; Association dimensionality reduction; K-means clustering; KNN. Model inference and averaging: Bootstrap and maximum likelihood methods; Bayesian method. Reinforcement learning: Basic concepts of reinforcement learning; Markov Decision Processes (MDP); Q-learning. Applications in Aviation: Air traffic demand forecasting; Flight delay prediction; Operations management and dynamic pricing; managerial implications and actionable insights with aviation case studies analysis. -- 1 of 3 --

目标

This subject will provide students with 1. the main concepts, ideas and techniques of advanced artificial intelligence (AI) in the aviation industry; 2. the essential principles, research methodology, data interpretation and data analysis with case examples in airline and airport operations; and 3. outlook of artificial intelligence development and its important in future air traffic and unmanned aircraft system traffic management.

先修要求

/ Co-requisite/ Exclusion Nil

Teaching Pattern

Methodology Teaching is conducted through lectures and case studies. The basic knowledge, research methodology and theoretical models will be introduced. The understanding of how to address and formulate problems using AI models and soft computing techniques is emphasised. Research methodology, data analytics skills, and algorithm design skills are taught in class as well as the related real-life scenarios using data to enhance their research abilities. Teaching/Learning Methodology Outcomes a b c d Lecture √ √ √ √ Case Study √ √ √ √