The School of Computer Science will offer the Summer School on Programming and Artificial Intelligence (AI) during the coming summer holiday (15 June – 3 July 2026). This intensive and dynamic summer programme is thoughtfully designed to equip students with essential knowledge and practical skills in programming, algorithms, data science, machine learning, and advanced artificial intelligence techniques. Students will benefit from the expertise of experienced lecturers from the School of Computer Science at UNNC, who will provide comprehensive instructions, hands-on coding labs, and real-world problem-solving sessions.
Progamme Highlights
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.
2. Interactive Learning Experience
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 (15 June – 3 July 2026), 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.
Core Module
Python Programming and Algorithms (PPA)
Module description: This module introduces basic principles of programming and algorithms. It covers fundamental programming constructs, such as types and variables, expressions, control structures, and functions. You will learn how to design and analyse simple algorithms and data structures that allow efficient storage and manipulation of data. You will also become familiar with basic software development methodology. You will learn intensively the principles during lectures and practise the programming skills during computer lab sections. You will spend around four hours each week in lectures and three hours in computer classes.
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
Teaching Objective
Upon successful completion of the Summer School, students will:
- 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.
Instructors
Dr Matthew Pike
Matthew Pike is an Associate Professor at the University of Nottingham, Ningbo, China. Based in the School of Computer Science (CS), he teaches a variety of modules including: Compilers, Programming and Human-Computer Interaction (HCI). Matthew studied his Master of Engineering in the School of Computer Science at Swansea University , which is situated in a beautiful part of South Wales, his home land. Matthew completed his undergraduate dissertation under the supervision of Dr Max L. Wilson, who subsequently moved to the University of Nottingham, UK. Matthew later went on to complete his PhD studies under Max’s supervision, at Nottingham University, UK. Matthew's working career has primarily been situated in academia. He did however have a brief stint in industry, working at Huawei Consumer Group, based in Shenzhen as a ‘User Experience Research Engineer’.
Dr. Kian Ming Lim
Dr. Kian Ming Lim is an Associate Professor at the School of Computer Science, University of Nottingham Ningbo China, and a Senior Member of the IEEE. His research focuses on Artificial Intelligence, with expertise in machine learning, deep learning, computer vision, few-shot learning, generative AI, and natural language processing. Dr. Lim has published over 100 peer-reviewed papers in computer science, bridging theoretical advancements with practical applications. Committed to academic mentorship, he actively guides the next generation of researchers.
Dr Chin Poo Lee
Dr. Chin Poo LEE joined the School of Computer Science at the University of Nottingham Ningbo China as an Assistant Professor in 2025. She began her academic career in 2006 as a Lecturer at the Faculty of Information Science and Technology, Multimedia University, where she was subsequently promoted to Associate Professor in 2023. In 2024, she joined the School of AI and Advanced Computing at Xi’an Jiaotong-Liverpool University as an Associate Professor.
Dr. Qian Zhang
Qian Zhang completed her BSc in Computer Science from the Hong Kong Baptist University in 2009. Following this she obtained an MSc (distinction) in Computer Science and Entrepreneurship at the University of Nottingham (UK) in the year 2010. Later in the next year, she started her PhD in the field of computer vision and image discovery within the Intelligent Modelling and Analysis Research Group (IMA) at the University of Nottingham, UK. She obtained her PhD in the year 2015 and joins the University of Nottingham Ningbo China (UNNC) in 2016 as an assistant professor.
Dr. Zheng Lu
Dr. LU Zheng joins UNNC as an Assistant Professor in 2017 after his assistant professorship at the City University of Hong Kong since 2013. Before working as a university faculty member, He was a Postdoctoral Research Fellow at University of Texas at Austin. He was an intern at Microsoft Research Asia from 2009 to 2010. He also worked as a Research Assistant under various research projects during his PhD candidature. He was a software engineer at Fuji Xerox Singapore Software Center from 2004 to 2006.
Dr. LU Zheng’s research work has been published in top international journals and conferences with hundreds of citations. Applications of his work has been reported by various media such as Wall Street Journal, Xinhua news, Straight Times, Daily Science, etc. He also serves regularly as program committee member and reviewer of top journals and conferences in the area of computer vision, image processing, multimedia, etc.
Dr. Huan Jin
My research interests focus on applying optimisation (LP/IP/MIP, Branch & Bound, Branch & Price, Nonlinear Programming) and machine learning techniques (Heuristics algorithms and Machine Learning algorithms) to solve large scaled real-world problem, including vehicle routing, transportation scheduling, network design, etc.
Dr. Tianxiang Cui
Dr.Tianxiang Cui is an assistant professor in the School of Computer Science at the University of Nottingham Ningbo China (UNNC) and a senior member of IEEE. Before joining UNNC, he was a senior AI engineer in Huawei and a senior algorithm researcher in PingAn. He was involved in some frontier industrial projects, including autonomous driving and quantitative trading. His main research interests include Computational Intelligence, particularly metaheuristic, evolutionary computation and neural networks; Machine Learning and Reinforcement Learning.
Dr. Qiao Lin
Qiao Lin received her PhD degree in computer science from University of Nottingham, Uk. Dr Lin is now a teaching fellow at School of Computer Science, University of Nottingham Ningbo China (UNNC). Prior to that, Dr Lin worked as an AI engineer in Tencent and research fellow in Wuhan University. Dr Lin’s research focuses on Trustworthy AI, Medical Image Segmentation, Uncertainty Analysis of AI Models, Fuzzy Logic and Large Models.
Programme Information
We sincerely welcome students from all backgrounds to apply for this programme
Tuition Fee: 12,000 RMB
Duration of Programme: 15 June – 3 July 2026
Please scan the QR code to fill in the application form.
Application & Payment Deadline: 22 May 2026
Contact Us
- For application please contact:
E-mail: Jane.WANG@nottingham.edu.cn
Tel: (0574) 88180000-8833
- For course content please contact:
E-mail: Qiao.Lin@nottingham.edu.cn
Jianfeng.Ren@nottingham.edu.cn
Tel: (0574) 88180000-6867
(0574) 88180000-8805