报告题目：
A Mathematical Introduction to Machine Learning
报告时间：
2022年9月13日 10：00
报告地点：
N204（现场上限50人）
腾讯会议室 ID：153-682-279
腾讯直播间：https://meeting.tencent.com/l/CwBAW6ZGfAy9
报告人：
鄂维南院士，北京大学
报告人简介：
鄂维南，北京大学国际机器学习研究中心、数学学院教授，中国科学院院士，美国数学学会、美国工业与应用数学学会Fellow。
研究领域为应用数学。在数学、应用数学、机器学习、物理、化学、力学等多个领域的顶级国际会议上作过邀请报告。2022 年国际数学家大会1小时报告人。2022年国际机器学习大会特邀报告人。2003年获国际工业与应用数学协会Collatz奖。2020年获ACM Gordon Bell奖。
报告简介：
Deep learning has changed the way we do artificial intelligence (AI) and is poised to change the way we do science. At the same time, it is generally perceived to be a collection of techniques or even tricks without a solid theoretical foundation. In this talk, we will try to address three questions: What is the magic behind neural network-based machine learning? How can we use deep learning to solve challenging problems in science and scientific computing? Can we formulate more general and maybe mathematically more natural models of machine learning?
The main message is that (deep) neural networks provide an effective tool for approximating high dimensional functions. This allows us to attack many difficult problems that are known to suffer from the curse of dimensionality. We will discuss the theoretical progress that has been made so far along these lines, and highlight the most pressing unsolved mathematical and practical issues.