Aug. 2 -Aug. 7, 2020 (1 week)
Latest Updates Due to COVID-19
The program co-organizers, Duke Kunshan University and St. Antony College of Oxford, have been closely monitoring the ongoing global outbreak of coronavirus. Both institutions have been proactively taking emergency measures in order to minimize health and safety risks to the students, faculty and staff members. Considering the unprecedented worldwide situation caused by the COVID-19 pandemic, we have to announce the cancelation of the Phase 1, two-week study at Oxford, of the 2020 Machine Learning Summer Program, but the Phase 2, one-week at DKU, will be remained.
It is hard to make this decision, but we are grateful for your cooperation and support. The faculty members at St. Antony College of Oxford will be still engaged in this program, delivering academic seminars online in your one-week study at DKU. Please find revised program information below
Based on the institutional partnerships between Duke Kunshan University and St. Antony College of Oxford, both universities have committed and worked collaboratively in delivering the Machine Learning Summer School in 2020. However, due to the ongoing Covid-19 pandemic, the program will be only conducted at Duke Kunshan University for one week: August 2-7, 2020.
Machine Learning nowadays has been attached great attention by academic institutions, intelligent manufactories, and digital business sectors. As a cutting-edge subject of AI, ML has been widely studied and developed by a large group of notable scholars and practitioners, and has a predictable application foreground in the region of business, industry, spaceflight and entertainments. “What is Machine Learning?” would be the fundamental learning objective of the MLSS program.
Diagnosing disease, predicting weather events, identifying fraud, personal assistants who anticipate our next desire (Alexa, Siri, Cortana, etc.), we have all heard about the new amazing things machine learning can do, but do you understand how these algorithms work and when we can expect them to work and, perhaps more importantly, when and why they fail? As a data scientist or machine-learning practitioner, you will be asked to explain how your algorithm works when it works and why your algorithm failed when it fails, and there will be times when it fails! We will discuss reasons algorithms may fail in this summer.
Upon successful completing of the one-week program, participant will receive a certification of attendance delivered by Duke Kunshan University.
Aug. 2-7, 2020丨Duke Kunshan University (70 seats)
The goal is to provide you with a foundation in fundamental concepts ubiquitous to machine learning – dimensionality, evaluating models, selecting model parameters, bias-variance trade-off, optimization – by discussing these concepts within the context of foundational machine learning algorithms and techniques. As this course emphasizes fundamental concepts in machine learning, we will not discuss advanced topics such as machine learning theory, reinforcement learning, deep learning, or adversarial networks.
What participants will learn from the program?
- Apply machine learning algorithms appropriately and effectively.
- Understand the strengths and limitations of the foundational machine learning algorithms.
- Identify opportunities for the use of machine learning techniques.
- Discuss potential approaches and challenges for specific applications.
- Design algorithmic workflows for specific applications.
- Implement machine learning systems using a high-level programming language.
|Applications open||Mar 2, 2020|
|Early Decision Payment Deadline||May 5, 2020|
|Early Decision Review/Interview||May 6-11, 2020|
|Early Decision Admission Announcement||May 15, 2020|
|Early Decision Payment Deadline||May 20, 2020|
|Normal Application Deadline||May 26, 2020|
|Normal application Review & Interview||May 27-30, 2020|
|Admission Announcement for Normal Applications||June 1, 2020|
|Payment Deadline||June 5, 2020|
Requirements and Prerequisites
- Rising senior or rising junior undergraduates in STEM majors.
- Prerequisites: Linear Algebra, Basic Probability, and Statistics.
- Good English proficiency required for non-native English speakers, TOEFL (80) or IETLS (6.5) preferred; online interviews will be conducted for Phase 1 applicants without TOEFL or IETLS scores.
- Excellent academic performance, minimum GPA 3.0/4.0.
- Strong interests in pursuing electrical and computer engineering graduate program at DKU preferred (Duke program).
Instructors at Duke Kunshan and Oxford
Fellow, Institute of Electrical and Electronics Engineers (IEEE)
Professor, Duke Kunshan University & Duke University
Associate Dean, Duke Kunshan University
Director, Master of Engineering (MEng) Program in Electrical and Computer Engineering, Duke Kunshan University
Director, Institute of Applied Physical Science and Engineering (iAPSE), Duke Kunshan University
Director, Data Science Research Center (DSRC), Duke Kunshan University
Xin Li received a Ph.D. in electrical and computer engineering from Carnegie Mellon University, Pittsburgh, P.A., in 2005, and M.Sc. and B.Sc. degrees in electronics engineering from Fudan University, Shanghai, in 2001 and 1998 respectively. He is a professor in the Department of Electrical and Computer Engineering at Duke University, Durham, N.C., leads the Institute of Applied Physical Sciences and Engineering (iAPSE), and is director of the Data Science Research Center (DSRC) at Duke Kunshan University. He developed and is now managing the master’s program in electrical and computer engineering at DKU. His research interests include integrated circuit, signal processing and data analytics. Dr. Li has invented numerous novel algorithms, tools and methodologies for data analytics that have been adopted and commercialized to produce products that have wide impacts, including smartphones, search engines, self-driving vehicles and virtual reality devices.
Areas of expertise
A large body of Li’s research focuses on data analytics for integrated circuit and semiconductor manufacturing where he led a number of impactful projects to model, analyze and optimize manufacturing variabilities.
Professor of Computer Science, University of Oxford
Deputy Head (Teaching) of the Department of Computer Science
Co-Director of the Autonomous Intelligent Machines and Systems Centre for Doctoral Training (AIMS CDT)
Member of the Cyber Physical Systems research group
Alex Rogers originally studied physics at Durham University before joining Schlumberger as a wireline logging engineer. After five years working in various oilfields around the world he took suspended employment to study for a Ph.D. in computer science applying statistical physics to models of evolving populations. Upon completing my Ph.D. he worked for a spin-out from the Santa Fe Institute applying complexity science to business problem before returning to academia, initially at the University of Southampton, and now at the University of Oxford.
Roger’s research applies artificial intelligence and machine learning within physical sensor systems to address real-world problems focusing on sustainability. His recent work has addressed future energy systems, such as the smart grid, citizen science platforms, and environmental monitoring, and typically involves the real-world deployment of novel approaches in devices, smartphones or the cloud. His current work addresses smart building energy management and the development of low-cost conservation technology.
Senior Research Fellow in Computational Social Science at the Oxford Internet Institute, University of Oxford
Turing Fellow at the Alan Turing Institute for Data Science
Research Fellow in Humanities and Social Sciences at Wolfson College, University of Oxford
Dr. Yasseri graduated from the Institute of Theoretical Physics at the University of Göttingen, Germany (2010), with a Ph.D. in complex systems physics at the age of 25. Prior to coming to Oxford, he spent two years as a postdoctoral researcher at the Budapest University of Technology and Economics, working on the social complexity of collaborative community of Wikipedia editors, focusing on conflict and editorial wars, along with big data analysis to understand human dynamics, language complexity, and popularity spread. Dr. Yasseri’s main research interest is in human dynamics, social networks and collective behavior.
Yasseri has interests in analysis of large-scale transactional data to understand human dynamics, government-society interactions, mass collaboration and collective intelligence, information and opinion dynamics, collective behavior, and online dating.
Program fee at Duke Kunshan: 7,600 RMB
Including tuition fee, on-campus lodging, two meals per day, site visits, insurance, welcome reception and farewell, etc.
The program fee will be fully deducted from the tuition for those students who will be enrolled in the ECE MEng program at DKU in August 2021.
Housing and Campus life
Location at Duke Kunshan: On-campus Residence Hall, No. 8, Duke Avenue, Kunshan, Jiangsu province, China