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 are graduate courses approved by University of Prince Edward Island and we will facilitate transfer credit to the extent possible.

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 Analysis 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 4 to 15, 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 18 to 29, 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 18 to 29, 2018

School Directors and Contact Information

Dr. Sami Khedhiri
School of Mathematical and Computational Sciences
University of Prince Edward Island
Charlottetown, Prince Edward Island
Canada C1A 4P3
Email: skhedhiri@upei.ca

Dr. Qiang Ye
School of Mathematical and Computational Sciences
University of Prince Edward Island
Charlottetown, Prince Edward Island
Canada C1A 4P3
Email: qye@upei.ca

Dr. Gordon MacDonald
School of Mathematical and Computational Sciences
University of Prince Edward Island
Charlottetown, Prince Edward Island
Canada C1A 4P3
Email: gmacdonald@upei.ca

Apply to the AARMS Summer School

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