2017 PKU Summer School on Data Science

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In recent years, we are experiencing rapid advances in information and computer technology, which contribute greatly to the exponential growth of data. To properly handle, process and analyze such huge and often unstructured data sets, sophisticated statistical and mathematical tools and efficient computing methods need to be developed. The aim of the 2017 PKU Summer School on Data Science is to introduce some of the recent developments of machine learning, statistics, applied mathematics and optimization techniques in high performance data analysis. The summer school will be held from July 10-24 at Peking University. It is sponsored by the NSFC Tianyuan Foundation, Key Lab of Mathematics and Applied Mathematics (PKU), School of Mathematical Sciences (SMS) and Beijing International Center for Mathematical Research (BICMR).

Courses:

Week 1 (July 10-15, 2017)
Suvrit Sra (LIDS@MIT)
Optimization for Machine Learning:
Convex and Nonconvex
Junming Yin (MIS@University of Arizona)
Probabilistic Graphical Models:
Inference, Learning, and Case Studies

Week 2 (July 17-24, 2017)
Peter Xi Chen and Rocky Duan
(CS@UC Berkeley)
Deep Reinforcement Learning
Martin Lindquist
(Biostatistics@Johns Hopkins University)
Principles of functional Neuroimaging
Mauro Maggioni
(Math@Johns Hopkins University)
Geometric and Graph methods for high-dimensional data

Organizing Committee:

Bin Dong(Peking University)
Zaiwen Wen (Peking University)

Recruit:

Upper division undergraduate students, graduate students and young scholars. Enrollment: 40-60
Enrollment: 40-60

Registration：

There is no registration fee. One recommendation letter is needed for the registration. Attendees should arrange his/her accommodation by himself/herself. We will provide the PKU meal card with partial meal allowance according to the total budget during your stay. The registration deadline is June 5th, 2017. The final admission list will be notified by email on June 15, 2017.
Registration Link: https://www.wenjuan.com/s/Yf6rEj/