一、学校简介
麻省理工学院(Massachusetts Institute of Technology)是天下闻名私立研讨型大学，停止至2018年10月，麻省理工学院的校友、教职工及研讨职员中，共发生了93位诺贝尔奖得主（天下第六） 、8位菲尔兹奖得主（天下第八）以及25位图灵奖得主（天下第二）。MIT素以顶尖的工程学而闻名，拥有浩繁顶级实行室，位列2016-17年天下大学学术排名（ARWU）工程学天下第一，被称为工程科技界的学术首领。QS2019资料迷信排名环球第一。
二、课程简介
麻省理工学院夏季学术课程共两个主题：
1）New Materials Design & Machine Learning
麻省理工学院资料迷信与工程学院(DMSE, MIT)中心实行室主理，由麻省理工学院人工智能/资料迷信学科的中心传授担纲课程设计和讲授任务。讲授团队包罗多名来自麻省理工学院人工智能实行室、资料迷信与工程实行室等中心科研讲授团队的资深传授。课程将重点存眷用呆板学习的办法反向发明新资料，以及资料迷信与其他穿插学科的前沿研讨偏向等外容，以Project Based Learning (PBL)讲授法睁开，讲授课程与麻省理工学院同期开设的相干学科课程内容同步。
2）MIT Artificial Intelligence for Financial Engineering
麻省理工学院斯隆办理学院(MIT Sloan School of Management)人工智能研讨团队主理，由麻省理工学院人工智能/金融工程学科的中心传授担纲课程设计和讲授任务。讲授团队包罗多名来自麻省理工学院人工智能实行室和斯隆办理学院等中心科研讲授团队的资深传授。课程将重点存眷人工智能对将来贸易社会的影响与应战，以及人工智能与贸易办理的穿插学科等外容。课程将以Project Based Learning (PBL)讲授法睁开，讲授课程与麻省理工学院同期开设的相干学科课程内容同步。
麻省理工学院斯隆办理学院被以为是美国最出色的商学院之一。麻省理工学院斯隆办理学院在2005年被《美国旧事与天下报道》杂志评比为美国排名第四的商学院，仅次于哈佛商学院、斯坦福大学商学院和宾夕法尼亚大学沃顿商学院。自从1914年兴办以来，麻省理工学院斯隆办理学院为九十多个国度培育了一万六千多名流才，此中百分之五十的人是初级办理职员，百分之二十的人是公司企业总裁，别的另有六百五十多人兴办了本人的公司。美国闻名至公司惠普电脑公司，波音飞机公司和花旗银行的总裁都是这所商学院的结业生。
2020暑假MIT New Materials Design & Machine Learning课程分为Pre-learning、On-campus Course、Post-learning三大局部，合计74个课时。
Pre-learning合计24个课时，须完成指定阅读资料及相干作业。
On-campus Course由两大模块构成——学术模块和探究模块。
学术模块合计50个课时，此中中心讲授局部24个课时及理论局部26个课时，中心传授局部以传授及助教的专业课为主，理论局部包罗学术项目、小组讨论、小组作业、中心实行室/机构看望等，在学习专业课程的同时，先生将无机会进入MIT中心实行室或波士顿外地行业抢先企业，愈加片面前瞻性地理解相干技能贸易化的开展历程。
探究模块由文明看望、Fellowship及主题Panel构成。波士顿作为美国东部的紧张都会，是美国的教诲之都、汗青之都、艺术之都、体育之都，在同窗们学习及探究波士顿的同时，由波士顿外地大先生构成的Fellowship将为先生提供全程的领导及帮忙，协助同窗们深化理解波士顿确当地生存及文明；同窗们还将无机会参与针对职业开展、科研、失业、创业等主题偏向的Panel，为同窗们将来开展提供新思绪及指点意见。
三、中心课程及简介
1）New Materials Design & Machine Learning
Course Description：
Computational Materials Science involves and enables the visualization of concepts and materials processes which are otherwise difficult to describe or even imagine. Among other things, this field of allows materials to be designed and tested efficiently.
Computational and analytical techniques are necessary for materials science and engineering topics, such as material structure, symmetry, and thermodynamics, materials response to applied fields, mechanics and physics of solids and soft materials. Presents mathematical concepts and materials-related problem-solving skills alongside symbolic programming techniques. Symbolic algebraic computational methods, programming, and visualization techniques; topics include linear algebra, quadratic forms, tensor operations, symmetry operations, calculus of several variables, eigensystems, systems of ordinary and partial differential equations, beam theory, resonance phenomena, special functions, numerical solutions, statistical analysis, Fourier analysis, and random walks.
Academic Syllabus：
The course begins with basic reviews of the foundations of Computational Materials Science before moving on to a more rigorous development of the theories and methodologies that underlie this novel field. Students will also have the chance to explore the applications of the theoretical portions of the program through lectures and site-visit opportunities.
Academic Module：
Module 1: Introduction of New Materials
Module 2: Advanced Machine Learning for Materials Science
Module 3: New Materials Intelligence
Module 4: Computer-driven design of molecular materials
2）MIT Artificial Intelligence for Financial Engineering
Course Description：
In order to compete in the rapidly developing financial sector, it is becoming increasingly necessary to make use of Machine Learning and Artificial Intelligence technologies to analyze massive amounts of data and predict trends.
Financial Engineering is a multidisciplinary field drawing from finance and economics, mathematics, statistics, engineering and computational methods. Machine Learning and Artificial Intelligence play a significant role in the creation of models and trading ideas from Renaissance and similar funds.
The main goal of this program is to provide the knowledge and practical skills necessary to develop a strong foundation on core paradigms and algorithms of Machine Learning and Artificial Intelligence, with a particular focus on applications of Machine Learning to various practical problems in Finance.
Students will learn the foundational methods in this field of study as well as have extensive opportunities to understand its implementation in real financial contexts.
Academic Syllabus：
With a focus on the organizational and managerial implications of these technologies, rather than on their technical aspects, this course will arm students with the knowledge and confidence students need to pioneer its successful integration in finance. The emphasis of this program will be on the use of simple stochastic models to price derivative securities in various asset classes including equities, fixed income, credit and mortgage-backed securities. This program will also consider the role that some of these asset classes played during the financial crisis.
Methodologies in artificial intelligence and data analysis will be introduced, after which students will gain an in-depth understanding through studies of applications of these technologies in innovative workshops and company visits.
Academic Module：
Module 1: Machine Learning and Artificial Intelligence in Finance
Artificial intelligence in finance is transforming the way we interact with money. AI is helping the financial industry to streamline and optimize processes ranging from credit decisions to quantitative trading and financial risk management.
Module 2: Financial Engineering and Arbitrage-based Pricing Models
The objective of the module is to introduce students to the modern framework for pricing of financial securities, including fixed income assets and derivatives. We cover the fundamental valuation concepts, pricing models, and methodological tools and applications.
Module 3: Financial Engineering and Risk Management
Financial Engineering is a multidisciplinary field drawing from finance and economics, mathematics, statistics, engineering and computational methods. The emphasis of FE & RM Part I will be on the use of simple stochastic models to price derivative securities in various asset classes including equities, fixed income, credit and mortgage-backed securities. We will also consider the role that some of these asset classes played during the financial crisis.
四、课程方式及稽核规范
课程方式：
|
Pre-learning（4周） |
On-campus Course（2周） |
Post-learning（4周） |
|
|
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稽核规范：
先生在麻省理工学院学习时期需经过两次项目规则的学术内容稽核，项目稽核评定规范如下：
* 按规则完成学习方案和义务且成果及格者将取得由官方发表的课程学习证书
五、课程讲授团队
1）New Materials Design & Machine Learning
1.W. Craig Carter
POSCO Professor, Department of Materials Science and Engineering, MIT
MacVicar Faculty Fellow
Research Interests: Computational Materials Science, Energy, Energy Storage
2. Markus Buehler
Department Head, Department of CEE, MIT
Jerry McAfee (1940) Professor in Engineering
Research Interests: Materials science and mechanics of natural and biological protein materials (materiomics)
3.Rafael Gomez Bombarelli
Toyota Professor, Department of DMSE, MIT
Research Interests: Computational Materials Science
4.Boris Kozinsky
Professor, John A. Paulson School Of Engineering and Applied Sciences, Harvard University
Research Interests: Computational Materials Science
2）MIT Artificial Intelligence for Financial Engineering
1.Leonid Kogan
Nippon Telegraph & Telephone Professor of Management
Professor, Finance, MIT Sloan School of Management
Director, MIT Laboratory for Financial Engineering
2.Andrew W. Lo
Charles E. and Susan T. Harris Professor, MIT Sloan School of Management
Director, MIT Laboratory for Financial Engineering
3.Kalyan Veeramachaneni
Principal Research Scientist, Department of EECS, MIT
Research Interests: Big data; Human data interaction; Impactful domains
六、项目特征
包罗但不限于理论效果后续跟进、助理研讨员请求、临时项目跟进等。
六、报名条件
报名须知：本项目总名额20人，报名停止日期2019年12月6日。项目方将在报名停止后一致构造签证操持，未操持护照的同窗请尽快于停止日期前操持护照。
七、项目工夫
Pre-learning：2020年1月4日-2020年1月31日
（以详细告诉日期为准，普通为动身前1个月）
On-campus Course：2020年2月1日-2月15日
（以上项目日期均为北京工夫，包括从国际航班降落至抵达国际全程15天。）
Post-Learning：2020年2月16日-3月8日
八、项目用度
1. 项目费: 5450 USD/人
2. 研讨生院将依据报告状况择优停止赞助
项目费包括：
(1)项目课程用度、项目实行室实行东西及资料用度、学习材料用度
(2)项目时期留宿用度（留宿规范为两人一间）
(3)餐饮用度（包括逐日早餐、局部午餐，合计20餐）
(4)在美交通（波士顿的接送机用度、在美时期的大众交通用度）
(5)文明探究（寓目外地体育竞赛的用度、观赏波士顿外地其他学校、博物馆、自在之路等景点的门票等）
(6) 国际保险用度
(7)美国签证请求帮忙（包罗项目主理方为先生操持约请函、签证用行程单等材料、面签培训指点等，此项为项目全体效劳的一局部，已有美签的不但独退还。）
项目费不包括：
(1)国际往复机票用度；
(2)团体美国签证用度；
(3)银行国际电汇手续费；
(4)团体破费；
九、报名资料
十、报名士程
3. 完成线上报名并取得登科邮件后，向研讨生院提交：
1）纸质版《北京理工大学在校研讨生出国（境）请求表（新版）》（详见附件一）
2）外语程度证明（四、六级/雅思/托福等成果单复印件）
3）在学时期各种嘉奖/获奖证明（复印件）
十一、项目征询
项目方征询微信：Bobbi，微信号：BostonMind
研讨生院：张教师 010-68913589
研讨生院培育办公室
2019年11月25日