Summer School on Programming and Artificial Intelligence (AI)

1. Comprehensive Curriculum

The programme covers four core modules designed to provide a strong foundation in programming, algorithms, machine learning, data science, and advanced AI topics.

Each module will comprise approximately 14 hours of engaging lectures and six hours of interactive computer lab sessions, ensuring that students gain both theoretical knowledge and practical hands-on experience.

A concise yet intensive three-week schedule (2 June – 20 June 2025), strategically designed for depth and efficiency, allowing students to quickly expand their computing and AI skills during the summer break.

Courses will be delivered by distinguished lecturers and researchers from the School of Computer Science.

Artificial Intelligence Methodologies and Applications (AIMA)

Module description: This module will teach fundamental theories and techniques of machine learning and artificial intelligence, and basic theories and principles on deep learning, with applications on various machine-learning problems. You will gain a broad overview of the fundamental theories and techniques of artificial intelligence, explore how computers can produce intelligent behaviour, and study topics such as search techniques, neural networks, data mining, and knowledge representation and reasoning. You will spend around four hours each week in lectures and three hours in computer classes.

    

Data Science with Machine Learning (DSML)

Module description: This module introduces students to the complete machine learning pipeline, from data preparation to model deployment. Beginning with fundamental concepts in data modeling and analysis, students learn to frame real-world problems for computational solutions. The curriculum emphasizes practical data science skills including data wrangling, preprocessing (handling missing values, feature engineering, and normalization), and effective visualization techniques for pattern discovery. Students explore core machine learning methodologies through both unsupervised learning and supervised learning approaches. The hands-on component reinforces theoretical concepts through practical implementation, covering data manipulation with Pandas, visualization using Matplotlib and Seaborn, and building machine learning models. The course delivery includes approximately four hours of weekly lectures complemented by two hours of lab sessions. Upon completion, students will have developed the necessary skills to solve complex, data-driven problems across various industry domains, with proficiency in implementing and evaluating machine learning solutions.

    

Advanced Topics on Artificial Intelligence (ATAL)

Module description: This module will teach students some advanced topics in machine learning and artificial intelligence, We study the latest advanced machine learning topics such as reinforcement learning, recurrent neural networks, pre-trained models, and large language models, etc.¸The objectives of this modules are to provide students in-depth knowledge and hands-on practice on artificial intelligence, and to lay the foundations for future master programme or PhD programme on topics related to machine learning and artificial intelligence. You will spend around four hours each week in lectures and three hours in computer classes.

    

* Schedule to change

• Demonstrate proficiency in Python programming and the ability to design and implement algorithms and data structures effectively.
• Acquire a solid understanding of fundamental and advanced machine learning techniques and their applications in real-world scenarios.
• Gain practical experience with data science methodologies, including data visualization, statistical analysis, and predictive modeling.
• Develop an understanding of cutting-edge artificial intelligence concepts and techniques, including deep learning, computer vision, and optimization.
• Strengthen their analytical, critical thinking, and problem-solving skills, preparing them for advanced studies and careers in AI, data science, software development, and related fields.

Prof. Dave Towey

Dave Towey received his BA and MA degrees from The University of Dublin, Trinity College; PgCHE from University of Nottingham; PgCertTESOL from The Open University of Hong Kong; MEd from The University of Bristol; and PhD from The University of Hong Kong. He was the first foreign academic recipient of the Zhuhai municipal outstanding teacher award, in 2007, and he received the Lord Dearing award in 2017 for his outstanding contribution to the development of teaching and student learning. He is a full professor, and the head of the School of Computer Science, which he joined in September 2013. He also serves as deputy director of the International Doctoral Innovation Centre (IDIC). He was previously the associate dean for education and student experience for the Faculty of Science and Engineering.

After graduating from The University of Dublin, Trinity College, Dave worked in Japan in the late 1990s, helping develop a breast cancer screening tool using ultrasound imaging technology and fuzzy reasoning.

After this, he lived in Hong Kong from 2000 to 2005/2006, where, as well as completing his PhD, he worked at The University of Hong Kong, and as a teacher and teacher trainer in the local school system.

In 2005, he became involved in a newly created liberal arts college in Zhuhai, Mainland China, the Beijing Normal University – Hong Kong Baptist University United International College (UIC), where he remained until 2013. While at UIC, he taught modules related to computer science, linguistics, and education. He also held several roles, including deputy director of the English Language Centre, and coordinator (director) of the Teaching English as a Second Language (TESL) degree programme. In these roles, he oversaw delivery of a large number of pre-service and in-service training courses and workshops.

Dave's research interests span a number of areas, including technology-enhanced teaching and learning, and software testing, especially metamorphic testing and adaptive random testing.