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.

Cloning the repository to 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" (, )

Lab 01: "Introduction to the Lab Environment" (, )

Lab 02: "Fundamentals of Python Programming" (, )

Lab 03: "Supervised Machine Learning"

• Naive-Bayes: (, )
• k-Nearest Neighbors: (, )
• Logistic Regression: (, )

Lab 04: "Unsupervised Machine Learning"

• K-Means: (, )
• Expectation-Maximization: (, )

Lab 05: "Deep Learning - Artificial Neural Networks (ANNs)" (, )

Lab 06: "Deep Learning - Convolutional Neural Networks (CNNs)" (, )

Running the Coding Challenge "Kick-Start" Notebook

Coding Challenge: "Data Download and Annotation Notebook" (, )

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