报告题目：Fundamentals of Supervised Machine Learning: From Linear Regression to Neural Networks
报告时间：2026年3月11日14:00
报告地点：雷丁楼C101
报告专家：Fazil Baksh
主持人：虞越越
报告专家简介：
Dr. Fazil Baksh is an academic and statistician serving as a Lecturer in the Department of Mathematics and Statistics at the University of Reading. His research focusses on machine learning and advanced statistical methods applied to medical and biological sciences, with particular emphasis on personalised medicine, genetics and computational modelling. He has authored several peer-reviewed articles that span both methodological innovation and practical application, contributing to the development of robust analytical tools for health, genomics, and precision medicine research.
报告内容简介：
This lecture introduces the fundamental ideas of supervised machine learning from a historical and methodological perspective, with a focus on the underlying mathematical concepts. Beginning with classical linear regression, we progressively explore more flexible models, including spline regression, penalised methods, local regression, and neural networks. The audience will be exposed to how modern machine learning builds on linear algebra, probability, calculus, and optimization, and how these concepts underpin contemporary artificial intelligence systems such as image recognition, language processing, and automated decision-making. The goal is to strengthen theoretical understanding while developing practical intuition for designing, analyzing, and evaluating AI-driven predictive models.
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雷丁学院
2026年3月3日
