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Introduction to Machine Learning class taught at ISU, Fall 2020 to Spring 2021

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MLClass

Introduction to Machine Learning class taught at ISU, Fall 2020 to Spring 2021 Syllabus

All materials, except recorded videos uploaded to Canvas due to privacy concerns, are posted in this repository.

Slides

Slides are made in Markdown source code and compiled into PDFs, webpages, etc., using Pandoc An example script to convert from Markdown to PDF is make.sh.

The source files in Markdown are always the latest. There maybe delays in PDF syncing.

Precompiled PDFs are given below:

  1. Introduction
  2. Linear classifiers
  3. Decision trees
  4. SVMs
  5. Regression
  6. Neural networks
  7. Clustering
  8. Deep Learning
  9. Reinforcement Learning
  10. Ensemble Learning
  11. CV, NLP, tiny ML

How to audit (for ISU students only)

Now students can request to audit a course electronically by going to AccessPlus, clicking on the Student tab, clicking on Registrar Forms, and clicking "Schedule Change Form". They will need to attach an e-mail from their instructor saying they have permission to audit the course. After attaching documentation, the form can then be routed for approval.

Setups hint for TAs

JupyterHub/TLJH

To setup the Jupyter Hub (TLJH), follow this instruction. If run into errors, try this first. Accessible only from on-campus or on-VPN.

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Introduction to Machine Learning class taught at ISU, Fall 2020 to Spring 2021

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  • Jupyter Notebook 95.7%
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