哥伦比亚大学教授计算机与人工智能科研课题暑期科研-2023

  　2023暑期线下科研·海外：哥伦比亚大学教授计算机与人工智能科研课题

　　开始日期: 2023-07-08

　　课时安排: 2周专业预修+2周在线科研+2周深⼊⾯授科研与实验室Workshop

　　Prerequisites 适合人群

　　适合年级 (Grade): 高中生/大学生

　　适合专业 (Major): 欲申请世界学校计算机科学、人工智能、机器学习、深度学习、数据科学、金融工程等相关专业的大学生及学有余力的高中生

　　Instructor Introduction 导师介绍

　　Miquel

　　哥伦比亚大学 Columbia University教授

　　Miquel导师现任哥伦比亚大学Adjunct Assistant教授、纽约大学Stern商学院Adjunct Assistant教授、Global AI 开发主管、国际能源论坛创新科技主管、西班牙高等管理学院(ESADE)金融大数据方向正教授。曾任瑞银集团(UBS)执行总裁(Executive Director)、安道尔银行CIO和首席投资顾问。研究领域包括商业分析、资产配置、大数据、机器学习在交易算法和金融科技中的应用。Miquel导师是一位商业分析与金融经济大数据领域的经验丰富，是一位在资产管理方面拥有20多年经验的金融科技经验丰富学者和实践者。

　　Miquel is a financial markets practitioner with more than 20 years of experience in asset management, he is the Founder of Artificial Intelligence Finance Institute. Head of Development at Global AI and co-Editor of the Journal of Machine Learning in Finance. He serves in the Advisory board of FDI and CFA Quant Investing Group.He worked for UBS AG (Switzerland) as Executive Director. He is member of European Investment Committee for the last 10 years. He worked as a Chief Investment Office and CIO for Andbank from 2000 to 2006. He started his career at KPMG. He is Adjunct Assist Professor at NYU Courant Institute of Mathematical Sciences and the CQF institute. He has been Adjunct Assist Professor at Columbia University teaching Asset Allocation, Big Data in Finance and Fintech. He is also Professor at ESADE teaching Hedge Fund, Big Data in Finance and Fintech. He taught the first Fintech and Big Data course at the London Business School in 2017.

　　任职学校 ...展开

　　Program Background 项目背景

　　机器学习是一门多交叉专业，将计算机作为工具实时模拟人类学习方式，将现有内容进行知识结构划分有效提高学习效率，知识内容涵盖概率论知识、统计学知识、近似理论知识和复杂算法知识。由于机器学习可以有效利用数据解决简单规则不能处理或者难以解决的问题，机器学习广泛应用在于搜索引擎、无人驾驶、机器翻译、医疗诊断、垃圾邮件过滤、人脸识别、数据匹配、信用评级等场景。项目将为围绕机器学习算法与其在生物医学监测领域的具体应用展开研究。

　　Program Description 项目介绍

　　项目中将重点探究器学习中的经典算法和深度学习中的神经网络的构成，导师将结合相关理论，以金融数据的处理为例，类比股票预测小程序，带领学生开发并优化自己的算法小程序并完成项目报告，进行成果展示。在此过程中，你将了解到人工智能及机器学习算法的广泛应用及其给软件工程带来的无限可能性。

　　学生将进入到世界学府-哥伦比亚大学，在为期两周的实地科研学习中与教授、Teaching Fellow面对面交流，在实验室中将理论与实践结合，沉浸式感受浓厚的学术氛围。用餐在校内食堂、住宿在学校宿舍中、生活在美丽、静谧的校园内，学生将真正零距离体验学校文化与生活方式。

　　With billions of mobile devices worldwide and the low cost of connected medical sensors, recording and transmitting financial data has become easier than ever. However, this ‘wealth’ of financial data has not yet been harnessed to provide actionable information. This is due to the lack of smart algorithms that can exploit the information encrypted within these ‘big databases’ of time series and take individual variability into account. Exploiting these data necessitates an in-depth understanding of the use of advanced digital signal processing and machine learning tools (e.g. deep learning) to recognize and extract characteristic patterns, and the ability to translate these patterns into actionable information. The creation of intelligent algorithms combined with existing and novel wearable and portable biosensors offers an unprecedented opportunity to monitor markets remotely and support the management of their condition. Data science to solve practical questions in this course you will learn about aspects of information processing including data preprocessing, visualization, regression, feature selection, classification (LR, SVM, NN), and their usage for decision support in the context of finance. The course aims to provide an overview of computer tools and machine learning techniques for dealing with financial datasets (time series). The course is practical with computer-based tutorials and assignments. The necessary theory will be covered. The lectures are divided as follows: ML basis, Popular classifiers, and Deep Learning.

　　Instructor Information 导师二介绍

　　J. G.

　　哥伦比亚大学 Columbia University教授

　　AI科技公司创始人和AI算法总负责人

　　致力于开发基于MRI的数据处理工具和计算建模方法

　　Syllabus 项目大纲

　　数据挖掘与处理：导师将首先列举复杂的金融数据，深入浅出至普适方法 Intro to financial applications; Data exploration and preprocessing

　　线性回归模型在分类和聚类中的作用 Linear models for regression; Linear models for classification

　　正则化; 分类器训练; 特征选择 Regularization; Training a classifier; Feature selection

　　训练和评估分类器：导师仍将以当下较火热的金融工程程序为例 Training and evaluating a classifier in a financial context

　　人工神经网络; 深度学习 Artificial neural networks; Deep learning RNN & CNN

　　项目回顾和成果展示 Program review and presentation

　　论文辅导 Project deliverable tutoring

　　Program Outcome 项目收获

　　2周专业预修+2周在线科研+2周深入面授科研与实验室Workshop

　　与诺贝尔奖得主交流机会

　　学术报告

　　学员获主导师Reference Letter

　　EI/CPCI/Scopus/ProQuest/Crossref/EBSCO或同等级别索引国际会议全文投递与发表指导(共同一作或独立一作可选)

　　结业证书

　　成绩单