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Ontario Tech acknowledges the lands and people of the Mississaugas of Scugog Island First Nation.

We are thankful to be welcome on these lands in friendship. The lands we are situated on are covered by the Williams Treaties and are the traditional territory of the Mississaugas, a branch of the greater Anishinaabeg Nation, including Algonquin, Ojibway, Odawa and Pottawatomi. These lands remain home to many Indigenous nations and peoples.

We acknowledge this land out of respect for the Indigenous nations who have cared for Turtle Island, also called North America, from before the arrival of settler peoples until this day. Most importantly, we acknowledge that the history of these lands has been tainted by poor treatment and a lack of friendship with the First Nations who call them home.

This history is something we are all affected by because we are all treaty people in Canada. We all have a shared history to reflect on, and each of us is affected by this history in different ways. Our past defines our present, but if we move forward as friends and allies, then it does not have to define our future.

Learn more about Indigenous Education and Cultural Services

February 24, 2016

Speaker: Dr. Nataliya Portman, Faculty of Science, Ontario Tech University
 The Art and Power of Data-Driven Modeling: Statistical and Machine Learning Approaches
Abstract: Ever-increasing volumes of data and computing power nowadays have led to the rise of machine learning, pattern recognition and data visualization methods. An idea of learning a mapping between predictor and outcome variables hidden in a high-dimensional training dataset has found applications in a variety of fields - image processing, medical imaging, bioinformatics, financial risk management, materials science to name a few. While machine learning seems to be put on a pedestal in data analysis (thanks to artificial intelligence enthusiasts), statistical learning still remains prevalent in certain areas such as clinical applications and epidemiology, for example. The choice of either statistical or machine learning approach depends on the example (training) dataset used. In this talk, I will discuss the differences between the two frameworks and illustrate their use on the assortment of problems drawn from my research experience in computational neuroscience and chemometrics. I will then give an overview of machine learning methods applied to the development of density functionals and materials property prediction based on data from quantum mechanical computations.