报告题目： Practical analysis of real data using machine learning
报告时间：2025年3月30日 （周日）16:00-17:00
报告地点：综合楼644会议室
报告人：Azhari A. Alhag (爱资哈里. 哈格)
报告人简介：
Azhari A. Alhag received the Ph.D. degree in applied Statistics from Saint Petersburg State University of Economics and Finance, Saint Petersburg, Russia, in 1993. He is an inspirational A. Professor dedicated to improving students learning development in mathematics and statistics sciences, resulting in achieving outstanding excellent results. He is also an expert in course delivery and developing engaging lectures for students, increasing student satisfaction and course enjoyment. His main research interest includes Statistical Learning, statistical models, fuzzy logic and fuzzy set theory, similarity and dissimilarity measures, fuzzy decision making, cubic sets, and their applications.
报告摘要：
In an era where data is often referred to as the "new oil," the ability to extract meaningful insights from it has become a critical skill across industries. However, real-world data is rarely clean, well-structured, or straightforward. It’s messy, complex, and often incomplete. This is where machine learning steps in as a powerful tool to uncover patterns, make predictions, and drive decision-making.
Today, we’ll explore the practical application of machine learning to analyze real data. Unlike idealized datasets often used in textbooks, real data comes with challenges―missing values, outliers, imbalanced classes, and noise. Our focus will be on how to navigate these challenges effectively, from preprocessing and feature engineering to selecting the right algorithms and evaluating model performance.
We’ll discuss real-world examples, such as predicting customer churn, detecting fraud, or optimizing supply chains, to illustrate how machine learning can transform raw data into actionable insights. Along the way, I’ll share best practices, common pitfalls, and tips for ensuring your models are not only accurate but also interpretable and scalable.
Whether you’re a data scientist, a business analyst, or simply someone curious about the potential of machine learning, this talk will provide you with practical tools and strategies to tackle real data problems head-on.
