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DTSTART;TZID=America/Detroit:20201201T100000
DTEND;TZID=America/Detroit:20201201T120000
DTSTAMP:20230905T171255Z
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UID:10000424-1606816800-1606824000@micde.umich.edu
SUMMARY:Introduction to Machine Learning
DESCRIPTION:OVERVIEW\n\n\nMachine learning is becoming an increasingly popular tool in several fields\, including data science\, medicine\, engineering\, and business. This workshop will cover basic concepts related to machine learning\, including definitions of basic terms\, sample applications\, and methods for deciding whether your project is a good fit for machine learning. No prior knowledge or coding experience is required \nINSTRUCTORS\nMeghan Richey\nMachine Learning Specialist\nInformation and Technology Services – Advanced Research Computing – Technology Services \nMeghan Richey is a machine learning specialist in the Advanced Research Computing- Technology Services department at the University of Michigan. She consults on several faculty and student machine learning applications and research studies\, specializing in natural language processing and convolutional neural networks. Before her position at the university\, Ms. Richey worked for a defense contractor as a software engineer to design and implement software solutions for DoD-funded artificial intelligence efforts. \nMATERIALS\n\n\n\nIntroduction to Machine Learning Topics\n\n\nA Zoom link will be provided to the participants the day before the class. Registration is required. \n\n\nInstructor will be available at the Zoom link\, to be provided\, from 9-10 AM for computer setup assistance. \nPlease note\, this session will be recorded.   \n\nRegister here \nIf you have questions about this workshop\, please send an email to the instructor at richeym@umich.edu
URL:https://micde.umich.edu/event/introductions-to-machine-learning-2/
LOCATION:Your Desktop
CATEGORIES:Data Science,Workshops
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201203T150000
DTEND;TZID=America/Detroit:20201203T160000
DTSTAMP:20230905T171255Z
CREATED:20230905T171255Z
LAST-MODIFIED:20230905T171255Z
UID:10000401-1607007600-1607011200@micde.umich.edu
SUMMARY:MICDE / IOE Seminar: Salar Fattahi\, Assistant Professor\, Industrial & Operations Engineering\, University of Michigan
DESCRIPTION:About Salar Fattahi: Dr. Salar Fattahi is an Assistant Professor in the Department of Industrial and Operations Engineering at the University of Michigan. He received his M.S. and Ph.D. degrees in Industrial Engineering and Operations Research from UC Berkeley. He received a M.S. degree from Columbia University\, and a B.S. degree from Sharif University of Technology\, Iran\, both in Electrical Engineering. Salar’s research lies at the intersection of optimization\, data analytics\, and control theory. He was the recipient of several awards\, including the 2020 INFORMS ENRE Best Student Paper Award\, 2018 INFORMS Data Mining Best Paper Award and 2020 Power & Energy Society General Meeting Best-of-the-Best Paper Award. He was also a finalist for the 2018 American Control Conference Best Paper Award. \nWebinar: LARGE-SCALE INFERENCE OF TIME-VARYING MARKOV RANDOM FIELDS: BRIDGING THE GAP BETWEEN STATISTICAL AND COMPUTATIONAL EFFICIENCIES \nContemporary systems are comprised of a massive number of interconnected components that interact according to a hierarchy of complex\, dynamic\, and unknown topologies. For example\, with billions of neurons and hundreds of thousands of voxels\, the human brain is considered as one of the most complex physiological networks\, whose structure remains as a long-standing mystery. As another example\, the emergence of self-driving cars has only accentuated the need for the development of real-time and reliable methods for detecting moving objects\, whose temporal locations are captured through a dynamically-evolving 3D network. Nonetheless\, the vast amounts of parameters to be estimated\, caused both by the large number of components and the time-varying nature of the systems\, are currently the major bottlenecks in our ability to successfully solve such inference problems. \nThe temporal behavior of today’s interconnected systems can be captured via time-varying Markov random fields (MRF). A popular approach to achieve this goal is based on the so-called maximum-likelihood estimation (MLE): to find a probabilistic graphical model\, based on which the observed data is most probable to occur. The MLE-based methods suffer from several fundamental drawbacks which render them impractical in realistic settings. First\, they often suffer from notoriously high computational cost in the massive problems\, where the number of variables to be inferred is in the order of millions\, or more. Second\, they fail to efficiently incorporate prior structural information into their estimation procedure. With the goal of bridging this knowledge gap\, the aim of this work is to revisit the standard MLE as the “Holy Grail” of the inference methods for graphical models\, and precisely pinpoint and remedy the scenarios where it fails. A recurring theme in our proposed approach is a class of efficiently-solvable mixed-integer optimization problems that is used in lieu of the regularized MLE for the inference of time-varying MRFs. Our proposed optimization problems enjoy strong statistical and computational guarantees\, while being amenable to a wide class of graphical models with different side information\, such as sparsity\, smoothness\, etc. \n\nThe MICDE Fall 2020 and Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nThis event will be a joint seminar with the University of Michigan College of Engineering’s Industrial Operations & Engineering department. \nQuestions? Email MICDE-events@umich.edu \nConnect via this Zoom link: https://umich.zoom.us/j/96516676892#success
URL:https://micde.umich.edu/event/micde-ioe-seminar-salar-fattahi-assistant-professor-industrial-operations-engineering-university-of-michigan/
LOCATION:Zoom Event
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/09/Salar-Fattahi.png
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201208T100000
DTEND;TZID=America/Detroit:20201208T120000
DTSTAMP:20230905T171254Z
CREATED:20230905T171254Z
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UID:10000386-1607421600-1607428800@micde.umich.edu
SUMMARY:Introduction to Deep Neural Networks with Keras/TensorFlow
DESCRIPTION:Deep Neural Networks (DNNs) are used as a machine learning method for both regression and classification problems. Keras is a high-level\, Python interface running on top of multiple neural network libraries\, including the popular library TensorFlow. In this workshop\, participants will learn how to quickly use the Keras interface to perform nonlinear regression and classification with standard fully-connected DNNs\, as well as image classification using Convolutional Neural Networks (CNNs). We will also look at regularization techniques and how to deal with under- and over-fitting. All examples will use Python; some familiarity with Python is recommended. The workshop will be done online via BlueJeans. We will run the models using Google Colab\, which requires a Google account.
URL:https://micde.umich.edu/event/introduction-to-deep-neural-networks-with-keras-tensorflow-9/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201208T150000
DTEND;TZID=America/Detroit:20201208T170000
DTSTAMP:20230905T171257Z
CREATED:20230905T171257Z
LAST-MODIFIED:20230905T171257Z
UID:10000425-1607439600-1607446800@micde.umich.edu
SUMMARY:Using Distill for R Markdown
DESCRIPTION:There are a variety of formats available for R markdown beyond ‘standard html’\, one of which is Distill.  Distill is specifically oriented toward presentation of results\, and offers a clean look with additional capabilities for citations\, marginal exposition\, and more.  One can even build an entire website or blog with this format.  This workshop will provide a brief overview and demonstration.\n\nhttps:m-clark.github.io
URL:https://micde.umich.edu/event/using-distill-for-r-markdown/
LOCATION:Your Desktop
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201208T170000
DTEND;TZID=America/Detroit:20201208T190000
DTSTAMP:20230905T171257Z
CREATED:20230905T171257Z
LAST-MODIFIED:20230905T171257Z
UID:10000426-1607446800-1607454000@micde.umich.edu
SUMMARY:Spatial regression models
DESCRIPTION:This lecture-style workshop will introduce relevant concepts and techniques for modelling cross-sectional data observed on regular (such as remote sensing pixels) or irregular (such as Census polygons) lattice. Such data often exhibits spatial dependence and is common across several fields. \n\nWe will cover the following topics: motivating examples from time series; spatial random fields and stationarity; spatial autocorrelation measures including Moran’s I; neighborhood or adjacency matrices that capture spatial dependence; and spatial autoregressive models including their estimation and interpretation. \n\nPlease note that the material will be discussed in a lecture style presentation with little or no hands-on components.  You should know linear regression well.
URL:https://micde.umich.edu/event/spatial-regression-models/
LOCATION:Your Desktop
CATEGORIES:Workshops
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