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When & Where
Date: 
Fri, November 22, 2019 - 9:00 AM to 12:00 PM
Location: 
D-Lab Convening Room
Description
Type: 

This workshop introduces Artificial Neural Networks (ANNs), a group of popular machine learning algorithms. No prior knowledge is required, though previous experience with other machine learning algorithms would be helpful. The workshop will be divided into three parts:

  1.  A brief history of ANNs and an explanation of the intuition behind them. This part aims to give the audience a conceptual understanding with few mathematical barriers, and no programming requirements.
  2. Step-by-step construction of a very basic ANN. Although the code will be written in Python, it will be intuitive enough for programmers of other languages to follow along. 
  3. Using the popular Python library scikit-learn, an ANN will be implemented on a classification problem. High-level libraries reduce the work for a researcher implementing ANN down to tuning a set of parameters, which will be explained in this part.

Prior knowledge: This workshop assumes familiarity with programming and an interested in machine learning. Completion of D-Lab's Python FUN!damentals or R FUN!damentals workshop series will be sufficient.

Technology requirement: It is suggested to install Python via Anaconda. Instructions can be found here.

Primary Tool: 
Python
Details
Training Host: 
D-lab Facilitator: 
Evan Muzzall
Format Detail: 
Hands-on, interactive
Participant Technology Requirement: 
Laptop

Basic Competency

These workshops are designed for participants with beginner fluency. They already have a little coding, tool or method experience but need to learn more intermediate applications such as conditional subsetting and appropriate data visualizations for their research. 

Examples: Introduction to Pandas, R-wrang, Data Visualization with Python, R-graphics, Survey Sampling, Weighting Data, Introduction to Qualtrics, Finding Health Statistics and Data, Data Viz Theory and Best Practices, Python Machine Learning, Machine Learning in R, Intro to Computational Text Analysis, Geospatial Fundamentals in Python/sf/QGIS/ArcGIS, Intermediate Tableau

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