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X-WR-TIMEZONE:America/Chicago
PRODID:-//University of Iowa//Events 1.0//EN
VERSION:2.0
CALSCALE:GREGORIAN
BEGIN:VEVENT
DTSTAMP:20231003T101348Z
DTSTART:20230523T120000
DTEND:20230523T140000
SUMMARY:Python for Data Analysis: Machine Learning Using Scikit-Learn with Python workshop
DESCRIPTION:1. Overview\n\nThis 2-session workshop is a gentle introduction to the practical applications of machine learning\, primarily using the Python package scikit-learn. The workshop is taught using JupyterLab in the Interactive Data Analytics Service (IDAS). \n\n2. Prerequisites\n\nParticipants are expected to be familiar with Python and JupyterLab. Theoretical (mathematical) knowledge of machine learning concepts is not required but may be helpful.\n\n3. Eligibility\n\nThis workshop is available to current University of Iowa members only. \n\n4. How to register\n\nClick HERE then log in with your HawkID and password. Click “Register now” at the bottom of the page to register. After registering successfully\, an automated email with a Zoom link will be sent to your University of Iowa email. Registration will close at 8 a.m. on Friday\, May 19\, 2023.\n\n5. Additional information \n\nIf you have any questions\, please see the workshop FAQs or contact research-computing@uiowa.edu.\n\n6. Workshop agenda\n\nThis workshop is taught in 2 sessions over 2 days. The later session builds on the previous one. Participants are encouraged to attend all sessions in order to learn the complete contents of the workshop.\n\nThis is not a theoretical (mathematical) introduction to machine learning\, nor is it a comprehensive introduction to all machine learning algorithms. The workshop focuses on the practical aspects of using Python for machine learning\, primarily with the package scikit-learn. If you are already familiar with the concepts below\, please see the workshop FAQs for a list of additional\, free learning resources.\n\nTentative topics to be covered:\n\nDay 1\n\nOverview of categories of machine learning\n Introducing scikit-learn\, a Python package commonly used for machine learning\n Training set and test set\n Supervised learning – Linear Regression\n Supervised learning – Gaussian Naive Bayes Classification\nDay 2\n\nSupervised learning – Gaussian Naive Bayes Classification (continued)\n Supervised learning – Nearest Neighbors Classification\n Unsupervised learning – K-means Clustering \n Unsupervised learning – Spectral Clustering (time permitted)\n\n\nhttps://events.uiowa.edu/78413
LOCATION:Online venue\, University of Iowa\, Iowa City\, IA 52242
UID:edu.uiowa.events-prod-78413
X-ALT-DESC;FMTTYPE=text/html:*1. Overview*

\n\nThis **2-session workshop** is a gentle introduction to the **practical** applications of machine learning\, primarily using the Python package *scikit-learn*. The workshop is taught using JupyterLab in the Interactive Data Analytics Service (IDAS).

\n\n*2. Prerequisites*

\n\nParticipants are expected to be familiar with Python and JupyterLab. Theoretical (mathematical) knowledge of machine learning concepts is not required but may be helpful.

\n\n*3. Eligibility*

\n\nThis workshop is available to **current University of Iowa members only**.

\n\n*4. How to register*

\n\nClick **HERE** then log in with your HawkID and password. Click “Register now” at the bottom of the page to register. After registering successfully\, an automated email with a Zoom link will be sent to your University of Iowa email. Registration will close at **8 a.m. on Friday\, May 19\, 2023**.

\n\n*5. Additional information *

\n\nIf you have any questions\, please see the workshop FAQs or contact research-computing@uiowa.edu.

\n\n*6. Workshop agenda*

\n\nThis workshop is taught in **2 sessions over 2 days**. The later session builds on the previous one. Participants are encouraged to attend all sessions in order to learn the complete contents of the workshop.

\n\n*This is ***not** a theoretical (mathematical) introduction to machine learning\, nor is it a comprehensive introduction to all machine learning algorithms. The workshop focuses on the practical aspects of using Python for machine learning\, primarily with the package scikit-learn. If you are already familiar with the concepts below\, please see the workshop *FAQs** for a list of additional\, free learning resources.*

\n\n**Tentative** topics to be covered:

\n\nDay 1

\n\n- Overview of categories of machine learning
\n - Introducing scikit-learn\, a Python package commonly used for machine learning
\n - Training set and test set
\n - Supervised learning – Linear Regression
\n - Supervised learning – Gaussian Naive Bayes Classification
\n

Day 2

\n\n- Supervised learning – Gaussian Naive Bayes Classification (continued)
\n - Supervised learning – Nearest Neighbors Classification
\n - Unsupervised learning – K-means Clustering
\n - Unsupervised learning – Spectral Clustering (time permitted)
\n

https://events.uiowa.edu/78413
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