Teaching

2024 Spring

Evolutionary Computation (Undergraduate/Master course)
Jan.–Apr., 2024, Birmingham, UK
The same introduction as the previous EC module.

2023 Spring

Evolutionary Computation (Undergraduate/Master course)
Jan.–Apr., 2023, Birmingham, UK
The same introduction as the previous EC module.

2022 Spring

Evolutionary Computation (Undergraduate/Master course)
Jan.–Apr., 2022, Birmingham, UK
Evolutionary Algorithms (EAs) are various biologically inspired randomised optimisation techniques (or randomised heuristics).
EAs aim to find a global optimum and assume little knowledge of fitness functions or how fitness functions are defined (the fitness function is what we want to optimise) compared with the gradient-based method (gradient descent).
EAs can provide more useful solutions for some real-world scenarios suitable for derivative-free methods to be solved.
In this module, I work as teaching assistant to help students understand the course in weekly Q&A meetings and help to mark the courseworks.

2022 Fall

Mathematical Foundations of Artificial Intelligence and Machine Learning (Undergraduate/Master course)
Sep.–Oct., 2022, Birmingham, UK
We will start with understanding basic mathematical structures in which data and machine learning models are formulated.
In this module, I work as teaching assistant to help students understand the course in weekly Q&A meetings and help to write bi-weekly quiz sheets for students.

2021 Fall

Machine Learning and Intelligent Data Analysis (Undergraduate course)
Sep.–Dec., 2021, Birmingham, UK
Machine learning studies how computers can autonomously learn from available data, without being explicitly programmed. The ‘information revolution’ has generated large amounts of data, but valuable information is often hidden and hence unusable. The module will provide a solid foundation to machine learning and advanced data analysis. It will give an overview of the core concepts, methods, and algorithms for analysing and learning from data. The emphasis will be on the underlying theoretical foundations, illustrated through a set of methods widely used in practice. This will provide the student with a good understanding of how, why and when do various modern machine learning and data analysis methods work.
In this module, I work as teaching assistant to help students understand the course in weekly Q&A meetings and help to mark the courseworks.