金沙js9线路中心2021-2022学年小学期安排课程《保险中的机器学习》，主要授课班级为2021级精算研究生班，同时向全院所有研究生及本科生开放。本次课程亦为中财－北工商“双一流”共建项目课程共享的建设内容，欢迎北工商感兴趣的同学参与课程学习。
该课程以R语言为授课基础，讲授机器学习监督学习与非监督学习基础、分类问题、决策树方法、支持向量机、主成分分析与因子分析、聚类方法等机器学习相关知识内容。
课程主讲人为英国伦敦城市大学贝叶斯商学院精算与保险系朱睿老师（Senior Lecturer in Statistics），她在IEEE Transactions on Neural Networks and Learning Systems、IEEE Signal Processing Magazine、IEEE Transactions on Image Processing、Pattern Recognition、Information Sciences等计算科学、信息科学的顶级期刊上发表文章近二十篇，常年讲授机器学习等相关课程。
授课时间为7月4日－8日（周一至周五），每晚18:10-20:45，三节连上。
腾讯会议信息如下：
7月4日周一：329171549
7月5日周二：692217377
7月6日周三：269188326
7月7日周四：410640854
7月8日周五：339777159
欢迎感兴趣的同学参与课程学习。
附：金沙js9线路中心2021-2022学年夏季小学期外聘教师开课情况表
姓名 |
朱睿 |
性别 |
女 |
工作单位 |
City, University of London |
国籍 |
中国 |
职务/职称 |
Senior lecturer |
学历 |
PhD in statistics |
研究领域 |
Statistical learning, subspace-based classification, image quality assessment, hyperspectral image analysis |
课程名称（中文） |
保险中的机器学习 |
课程名称（英文） |
Machine Learning in Insurance |
授课语言 |
中英双语 |
学分 |
1学分 |
课程日期 |
课程开始：2022年7月4日 课程结束：2022年7月8日 |
教师简介
|
Rui Zhu is a senior lecturer in statistics in the Faculty of Actuarial Science and Insurance, Bayes Business School, City, University of London.She received the Ph.D. degree in statistics from University College London in 2017. Her research interests are in statistical learning and its interdisciplinary applications, including classification and dimension reduction for high-dimensional data, distance metric learning, spectral data analysis, image quality assessment and hyperspectral image analysis. |
课程简介
|
This module will introduce you with fundamental concepts of machine learning and popular machine learning algorithms and their implementations in R. We will learn famous supervised learning and unsupervised learning algorithms and how to use them to solve real-world problems. We aim to understand the intuitions of the algorithms. We will also know how to use these algorithms in R to complete tasks with real-world datasets. |
教学大纲和进度安排
|
Lecture 1: An introduction to machine learning ·Some real-world machine learning applications ·An introduction to supervised learning and unsupervised learning ·Some trade-offs to consider ·From linear regression to logistic regression Lecture 2: k nearest neighbours, model assessment and cross validation ·k nearest neighbours ·How to evaluate a classification model ·How to determine the value of k ·Caret package Lecture 3: Tree-based methods, support vector machine ·Tree-based methods ·Support vector machine ·Examples in R Lecture 4: Principal component analysis, Factor analysis ·Principal component analysis ·Factor analysis ·Examples in R Lecture 5: K-means clustering, model-based clustering ·K-means clustering ·Model-based clustering ·Examples in R |
教材及参考资料 |
James, Gareth, et al. An Introduction to Statistical learning: with Applications in R. Springer, 2017. Hastie, Trevor, et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2017. Bishop, Christopher. Pattern Recognition and Machine Learning. Springer, 2009. |
