Data Science 101 Series: Machine Learning with Python

Founded in 2008, WiSER (Women in Science, Engineering & Research) is a sub-community of WISEST at the University of Alberta aimed at serving the needs of graduate students and early-career professionals. The community is comprised of individuals in the fields of science, technology, engineering, and mathematics (STEM) from the academia, government, and industry.WISER supports the retention and advancement of women and under-represented groups in STEM careers by providing opportunities for networking, mentoring, information exchange, and professional development. To this end, WISER encourages participation of everyone in order to promote the awareness of gender diversity and inclusion.

WiSER (Women in Science, Engineering & Research)

This is a 2-Day Machine Learning (ML) workshop which enables participants to work on interdisciplinary ML applications.

<p><em><strong>Note:  Refund can be requested up to 7 days before the event. If you purchase tickets for May 6, the latest date you can request a refund is April 29. If you purchase tickets for May 13, the latest date you can request a refund is May 6.  Eventrbrite&#x27;s fee ($1.30 per ticket) is not refundable. </strong></em></p><p>This workshop dives into the basics of machine learning using an approachable and well-known programming language, Python. We will be reviewing two main components:</p><ul><li>Day 1 (May 6): participants will get a general overview of ML topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. </li></ul><ul><li>Day 2 (May 13):  participants practice with real-life examples of ML and see how it affects society in ways you may not have guessed!</li></ul><p><strong>Note: Participants can register for either day or both days. </strong></p><p>By the end of this workshop, participants will be able to identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to train an ML model on the data.</p><h2>Recommended Preparations</h2><p>It is recommended that you read through the <a href="https://wiseruofa.files.wordpress.com/2021/04/pre-reading-materials.pdf" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">resource (PDF)</a> prior to the workshop to become familiar with Python and Pandas library.</p><h2>Speaker</h2><p></p>

<h3>Sara Soltaninejad, PhD</h3><p>Machine Learning developer, AltaML</p><p>Sara Soltaninejad has a PhD from the Department of Computing Science at University of Alberta and a Master from Shiraz University in Artificial Intelligent and Machine Learning. Her primary area of research is in Machine Learning and Computer vision. Her research interest is interdisciplinary applications of Machine Learning and  Computer vision especially in the medical field. Currently, she is a Machine Learning developer at AltaML, and is involved in applied research projects in Computer Vision and Machine Learning.</p>

<h3>Navaneeth Kamballur Kottayil, PhD</h3><p>Lead Machine Learning developer, AltaML</p><p>Navaneeth Kamballur Kottayil has a PhD from Computing Science, University of Alberta and MTech in Image Processing and Embedded Systems from Indian Institute of Technology, Kharagpur. His primary area of research is in Computer Vision and has over 20 peer reviewed publications and patents. His research interests are in the field of high dynamic range imaging, influence of content on image quality, perceptual quality analysis, Deep Learning, Image Processing, and Computer Vision. Currently, he is a Lead Machine Learning Developer at AltaML, and is involved in applied research projects in Computer Vision and Machine Learning.</p>

<h3>Graham Ericson, MSc</h3><p>Lead ML developer, AltaML</p><p>Graham has been working with applied AI for ten years in a variety of domains. He started with the Aries Lab at the University of Saskatchewan and researched Machine Learning in digital entertainment at the University of Alberta. He has four publications related to data mining and Machine Learning. Graham’s industrial experience has focused on applied Machine Learning with domains across ecommerce, healthcare, finance, MLOps, and operations. His machine learning expertise is in Bayesian Inference, Machine Learning Systems Development, and Unsupervised Learning. Currently, he is a senior Machine Learning Developer at AltaML, and is involved in applied research projects in Machine Learning.</p>

<h2>Sponsor</h2><p>This event is sponsored by <a href="https://www.bioware.com/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Bioware</a>.</p>

Data Science 101 Series: Machine Learning with Python

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Data Science 101 Series: Machine Learning with Python

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