July 18-Aug. 7, 2020 (3 weeks)
Phase 1: Oxford University: July 18- 31, 2020
Phase 2: Duke Kunshan University : Aug. 1-7, 2020
Based on the institutional partnerships between Duke Kunshan University (DKU) and St. Antony College, Oxford, both universities have committed to work collaboratively to deliver the Machine Learning Summer School (MLSS) in 2020. The program has been designed as a cross-border initiative that includes two phases: two weeks at Oxford and one week at DKU.
Sectors including academics, smart manufacturing and digital business are paying great attention to machine learning. A cutting-edge subject of artificial intelligence, machine learning is widely studied and developed by a large group of notable scholars and practitioners, and it has a predictable application foreground in the region of business, industry, spaceflight and entertainment. “What is Machine Learning?” is the fundamental learning objective of the MLSS program.
Diagnosing disease, predicting weather events, identifying fraud, personal assistants who anticipate our desires (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, 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 required to explain how your algorithm works – or why it failed. We will discuss the reasons algorithms may fail in this program.
The program will be discussion-oriented, requiring a high degree of participation by students in the classroom and extracurricular activities. Upon successful completing of the program, participants will receive a certification of attendance jointly from DKU and St. Antony College, Oxford, as well as an official transcript with two summer credits issued by DKU. (Summer credits will not count toward DKU/Duke degree requirements.)
Phase 1: July 18- 31, 2020 丨Oxford University (30 seats)
Students choosing Phase 1 at Oxford will be introduced to linear algebra and probability theory in order to prepare them for Phase 2 training in data science. The course consists of eight days of teaching: three hours a day of lectures in the morning, and extracurricular activities in the afternoons. The two-week curriculum has been designed with emphasis on topics more directly related to the field of data science; for example, using matrix algebra and the related matrices to linear transformations, analyzing finite and infinite dimensional vector spaces and subspaces, computing inner products and determine orthogonality on vector spaces, and interpreting statistical data using appropriate probability distributions.
Phase 2: Aug. 1-7, 2020丨Duke Kunshan University (70 seats)
The goal of this week 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?
In two weeks at Oxford, students will be able to:
- Recognize the concepts of the terms span, linear independence, basis and dimension, and apply these concepts to various vector spaces and subspaces.
- Use matrix algebra and the related matrices to linear transformations.
- Compute and use determinants.
- Compute and use eigenvectors and eigenvalues.
- Determine and use orthogonality.
- Use technological tools such as computer algebra systems or graphing calculators for visualization and calculation of linear algebra concepts.
- Analyze finite and infinite dimensional vector spaces and subspaces over a field and their properties, including the basis structure of vector spaces.
- Use the definition and properties of linear transformations and matrices of linear transformations and change of basis, including kernel, range and isomorphism.
- Compute inner products and determine orthogonality on vector spaces, including Gram-Schmidt orthogonalization.
- Identify self-adjoint transformations and apply the spectral theorem and orthogonal decomposition of inner product spaces, the Jordan canonical form to solving systems of ordinary differential equations.
- Organize, present and interpret statistical data, both numerically and graphically.
- Use various methods to compute the probabilities of events.
- Analyze and interpret statistical data using appropriate probability distributions; for example, binomial and normal.
- Apply central limit theorem to describe inferences.
- Construct and interpret confidence intervals to estimate means, standard deviations and proportions for populations.
- Perform parameter testing techniques, including single and multi-sample tests for means, standard deviations and proportions.
- Perform a regression analysis, and compute and interpret the coefficient of correlation.
In one week at DKU, students will be able to:
- 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||March 2, 2020|
|Early decision deadline||April 20, 2020|
|Early Decision Review/Interview||April 20-25, 2020|
|Early Decision Admission Announcement||April 28|
|Early Decision Payment Deadline||May 5|
|Normal application deadline||May 8, 2020|
|Normal Application Review & Interview||May 11-15|
|Admission Announcement for Normal Applications||May 18|
|Payment Deadline||May 25|
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).
- Only 30 seats for Phase 1, 70 seats for Phase 2.
- Rising juniors should at least apply for Phase 1, or both.
- Qualified rising seniors can apply for Phase 2 only.
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 Oxford (Phase 1): £3,180 (GBP)
Including tuition fee, lodging, two-day study visits, visa application service fees, group pickup to and from the airport by private coach, entrance fees for all Oxford site group visits, international insurance fees, welcome tea and gala dinner.
Not including: airfare, visa fee, lunch and dinner, etc.
Program fee at Duke Kunshan (Phase 2): 7,600 RMB
Including tuition fee, on-campus lodging, two meals per day, site visits, insurance, welcome reception and farewell, etc.
Phase-2 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 Oxford: Liddell Building - bedroom only with self-catering facilities at Christ Church College, Oxford
Liddell Building (1991) is situated in a quiet area of East Oxford, about a five-minute walk from amenities and 15 minutes from Christ Church College, Oxford. Check-in at Porters’ Lodge, located behind the gated entrance. The lodge reception is staffed 24-hours a day.
Comfortable, spacious standard single bedrooms in three- or four-bedroom apartments with shared bathroom and kitchen facilities. (Images for illustrative purposes only. Bedroom layout and size vary.)
Liddell Building – Bedroom, Oxford
Grounds and gardens
Liddell Building is built around a beautiful lawn and adjoins the university sports ground. In nice weather, it is possible to have a picnic on the grounds (note: no ball games). All guests are welcome to visit Christ Church College and enjoy the unique atmosphere of the college and cathedral free of charge (walking maps are available from Porters Lodge).
Location at Duke Kunshan: On-campus Residence Hall, No. 8, Duke Avenue, Kunshan, Jiangsu province, China