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DTSTART;TZID=America/Detroit:20201201T100000
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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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DTSTART;TZID=America/Detroit:20201208T100000
DTEND;TZID=America/Detroit:20201208T120000
DTSTAMP:20260607T000711
CREATED:20230905T171254Z
LAST-MODIFIED:20230905T171254Z
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
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201208T150000
DTEND;TZID=America/Detroit:20201208T170000
DTSTAMP:20260607T000711
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
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201208T170000
DTEND;TZID=America/Detroit:20201208T190000
DTSTAMP:20260607T000711
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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