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A series of interactive labs we prepared for the Introduction into Artificial Intelligence and Machine Learning course. The content of the series is based on Python, IPython Notebook, and PyTorch.

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README.md

MBI Master Course :: "Introduction into Artificial Intelligence and Machine Learning"

A series of interactive lab notebooks we prepared for the 7.044.1,00 Introduction into Artificial Intelligence and Machine Learning course offered in the Master of Arts in Business Innovation (MBI) at the University of St.Gallen (HSG).

The content is build on a series of Jupyter Notebooks based on Python, IPython Notebook, Scikit-Learn and PyTorch.

License: GPL v3

Course Banner

Cloning the repository to Azure Notebooks: Azure Notebooks

This is currently work in progress so expect minor errors and some rough edges ;)

Running the Lab Notebooks

Lab 00: "Testing the Lab Environment" (Binder, Open In Colab)

Lab 01: "Introduction to the Lab Environment" (Binder, Open In Colab)

Lab 02: "Fundamentals of Python Programming" (Binder, Open In Colab)

Lab 03: "Supervised Machine Learning"

  • Naive-Bayes: (Binder, Open In Colab)
  • k-Nearest Neighbors: (Binder, Open In Colab)
  • Logistic Regression: (Binder, Open In Colab)

Lab 04: "Unsupervised Machine Learning"

  • K-Means: (Binder, Open In Colab)
  • Expectation-Maximization: (Binder, Open In Colab)

Lab 05: "Deep Learning - Artificial Neural Networks (ANNs)" (Binder, Open In Colab)

Lab 06: "Deep Learning - Convolutional Neural Networks (CNNs)" (Binder, Open In Colab)

Running the Coding Challenge "Kick-Start" Notebook

Coding Challenge: "Data Download and Annotation Notebook" (Binder, Open In Colab)

Getting Started

Install dependencies via pip install -r requirements.txt.

Questions?

Pls. don't hesitate to send us all your questions using the course mail address:

aiml-teaching ( dot ) ics ( at ) unisg ( dot ) ch

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A series of interactive labs we prepared for the Introduction into Artificial Intelligence and Machine Learning course. The content of the series is based on Python, IPython Notebook, and PyTorch.

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