2017 Duke-Tsinghua Machine Learning Summer School

Deep Learning for Big Data

Group photo of 2016 Machine Learning Summer School Participants

Program Dates: July 25th to August 3rd, 2017
Location: Duke Kunshan University, Kunshan, China

The 2017 Duke-Tsinghua Machine Learning Summer School (MLSS) will bring you the opportunity to interact with some of the leading practitioners in one of the most important recent areas of Machine Learning---Deep Learning. The summer school will be characterized by hands-on training and lectures. The emphasis of the class discussions will be on helping to understand the underlying motivation for deep models and why they work. This will be facilitated by a focus on generative statistical models, and how these relate to efficient neural network implementations. A unique aspect of this class will involve the integration of Bayesian and optimization perspectives of deep learning.

Students will receive a thorough introduction to the latest methods in deep learning, with application to large-scale data sets (“big data”). The class will be “hands on,” characterized by practical code development and application. A student or professional attending this class will learn how to apply state-of-the-art machine learning methods to real problems in their research and/or in the business career.

You will also be earning a Duke-Tsinghua Certificate upon successful completion of the program requirements.

During the program, you will learn:

• The latest techniques for deep model development and techniques for model learning.
• Efficient methods for inference of latent model parameters, for model application to real data
• The underlying statistics that motivates these models
• How to apply the techniques in code development

Benefits for attending the course

Participants who take this Summer School will receive a thorough introduction to the application of machine learning methods to the analysis of massive data sets (“big data”). A particular focus will be placed on methods that employ “deep” or hierarchical models. Participants will learn techniques that are achieving state-of-the-art performance in image, video and text analysis. In addition to classroom instruction, participants will be given hands-on training in code development and testing, with application to real datasets.

Requirements and Prerequisites

We are looking for both current students and working professionals to join our program.

Applicants should have knowledge of basic statistics at the level of a first-year graduate student. Prior knowledge of machine learning (model building, learning methods, and inference) is useful, but not required. A strong background in applied mathematics at the first-year graduate level is also needed. Experience with computational coding is important, using Python, C++, or R is needed (Python preferred). Applicants are required to bring a laptop computer capable of doing computational analysis, with Python the most desirable coding platform.

The class will be taught in English, and therefore each student must be proficient in English communication.

Program Fee

Program fee includes tuition, on campus housing & meal, excursion to Shanghai, hotel in Shanghai (Meals in Shanghai are not included).

10% discount will be offered to all Early Action applicants. Current university students are offered student discount, which is covered by Duke Kunshan University fellowship.

 

	Early Application*	Normal Application
Professional	13,600	14,960
Student	6,800	7,480

 *Seats are limited, apply early.                      (All in RMB)

Duke Kunshan University is able to issue receipt for either meeting service or training fee.

The admitted students will receive an invitation letter for the program.

Important Dates

Admission Open	February 2rd , 2017
Early Action Deadline	March 10th, 2017
Early Action Interview	March 20th - March 31st, 2017
Early Action Admission Decision Announcement	April 7th, 2017
Early Action Deposit Deadline	April 21st, 2017
Normal Application Deadline	April 21st, 2017
Application Interview	May 2rd – May 12th, 2017
Application Admission Decision Announcement	May 19th, 2017
Normal Application Deposit Deadline	June 2rd, 2017