The summer school is intended for graduate students and promising undergraduate students from all parts of the world. Each participant is expected to register for at least two of the four courses. Each course consists of three hours lecture sessions per day for two weeks. These courses are certified to be graduate level courses by both the School of Mathematical and Computational Sciences (SMCS) at University of Prince Edward Island and by the Atlantic Association for Research in the Mathematical Sciences (AARMS). Upon successful completion of a course, SMCS and ARMS will award a certificate conforming this, which students can then take back to their home institutions if they plan to receive credit for these courses towards their degree. For student attendees, the AARMS Summer School will pay the tuition and the accommodation expenses, but will not cover the cost of travel. Note that the accommodation is provided only when a student is taking a course (the night before the first lecture of a course is covered). If a student arrives early or leaves late, the extra accommodation is not covered. In addition, those who will finish their bachelor/master degree in the spring/summer term of 2018 and will enter the graduate school in the fall term of 2018 are also considered students.
The summer school also has a limited number of non-student spots. If you are not a student and would like to take the courses offered at the summer school, you need to pay $600 for each course and cover your accommodation/travel.
To apply for AARMS Summer School 2018, please complete the online application form before April 15, 2018.
Please note that the evaluation committee of the summer school will review all applications shortly after Apr. 15, 2018 and thereafter notify the applicants whose applications are approved by email. Please do not book your air tickets before receiving the approval email.
In addition, the course dates have been revised slightly. Therefore, please pay attention to the dates when you apply for the summer school!
Functional Data Analysis for Big Data
Dr. Jiguo Cao
Canada Research Chair in Data Science
Simon Fraser University
Functional data analysis (FDA) is a growing statistical field for analyzing curves, images or any manifold objects, in which each random function is treated as a sample element. Functional data can be commonly found in many big data applications such as fitness data from the wearable device, air pollution, longitudinal studies, time-course gene expressions and brain images. This summer course will cover the major FDA methods such as nonparametric regression methods, functional principal component analysis, functional linear regression models, clustering and classification of functional data. All these methods will be demonstrated with real data applications. I will also show how to program for these methods in R.
June 7 to 20, 2018
Statistical Learning for High Dimensional Data
Dr. Wenqing He
University of Western Ontario
This course is intended to present an introduction to statistical analysis with high dimensional data (or big data) which arise often in almost everywhere nowadays with the advancement of new technologies. The methods to extract useful information from those big data are in great demand. Statistics is a science that deals with data, and therefore it plays a vital role in extracting information from big data. We will introduce the basic statistical analysis of different methods for obtaining information from high dimensional data and we apply these methods to a gene expression data example. The topics to be covered are: 1) features of big data and the problems for analyzing such data, 2) basic variable screening methods, 3) popular variable selection methods (Lasso, LARS, etc.), supervised learning methods (LDA, logistic model, classification tree, random forest), and unsupervised learning methods (clustering methods).
June 18 to 29, 2018
Machine Learning and Data Mining
Dr. Mark Schmidt
University of British Columbia
We introduce basic principles and techniques in the fields of data mining and machine learning. These are some of the key tools behind the emerging field of data science and the popularity of the "big data" buzzword. These techniques are now running behind the scenes to discover patterns and make predictions in various applications in our daily lives. We'll focus on many of the core data mining and machine learning technologies, with motivating applications from a variety of disciplines.
June 4 to 15, 2018
Foundations in Data Science and Applications
Dr. Osmar Zaiane
University of Alberta
Data Science is an interdisciplinary field concerned with the development of methods and processes to extract knowledge and insights from large collections of data in different forms and often from disparate sources. Since we are continuously collecting ever-growing collections of data, data-driven applications are becoming paramount in our economy and society. Data science is at the confluence of many disciplines, including data mining. The objective of the course is to provide an introduction to knowledge discovery in databases and complex data repositories and to present basic concepts relevant to real data mining applications, as well as reveal important research issues germane to the knowledge discovery domain and advanced mining applications. The course will also present hands-on exercises to apply the concepts on real cases. The participants will understand the fundamental concepts underlying knowledge discovery in data and gain hands-on experience with implementation of some data science algorithms applied to real-world cases.
June 4 to 15, 2018