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Sessions

Overview

Course Sessions

Find the Google Colab notebooks for the course sessions here.

Click on “Open in Colab” to open the notebook in Google Colab. You will be prompted to save it to your own drive. When saved to your own drive, you can make your own edits and changes to the notebook.

How to use this page

Demo notebook

A quick introduction to Google Colab and Notebooks Open in Colab

Portfolio

For Tuesday, Wednesday and Thursday, you are given an hour to work on a portoflio.

Read more about the requirements and how to hand in your portfolio here.

The assignments for each day are gathered in this notebook (links to Google Colab): Portfolio assignments

Remember to click “Copy to Drive” to create your own copy to work with!

Monday

An introduction to Python and Data Science (content)

  1. An introduction to Python and Data Science Open in Colab

  2. Python basics Open in Colab

  3. Pandas Open in Colab

  4. Visualization Open in Colab and this intro

  5. Wrap-up Open in Colab

Tuesday

Unsupervised Machine Learning I: Introduction to Exploratory Data Analysis

Introduction to Machine Learning Open in Colab

Unsupervised Machine Learning II: Finding Patterns in Messy Data Using Clustering

Clustering - a world of patterns Open in Colab

Supervised Machine Learning I: Introduction to Supervised Machine Learning

Introduction to Supervised Machine Learning (slides)

Supervised Machine Learning II: Prediction and Classification (and how to use it in your research)

Hands-on Tutorial in Supervised Machine Learning Open in Colab

Portfolio work: Unsupervised and supervised machine learning

For portfolio work today, we are focusing on unsupervised and supervised machine learning.

Link to portfolio notebook: Portfolio assignments

Remember to click “Copy to Drive” to create your own copy to work with!

Requirement for Tuesday: Work on solutions for either “unsupervised machine learning with penguins” or “clustering” and either “supervised machine learning with penguins” or “employee turnover”.

Wednesday

Introduction to Network Analysis

Introduction to network analysis Open in Colab

Geospatial Data and Mapping

Time for spatial stuff Open in Colab

Portfolio work: Network analysis and geospatial data

For portfolio work today, we are focusing on network analysis and working with geospatial data.

Link to portfolio notebook: Portfolio assignments

Remember to click “Copy to Drive” to create your own copy to work with! (or copy new assignments into your existing notebook)

Requirement for Wednesday: Work on solutions for either the network analysis case study 1 or case study 2 and the exercise for spatial stuff.

Thursday

NLP I: Text for Exploratory Data Analysis

Getting started with text Open in Colab

Getting Tweets Open in Colab

NLP II: Using Text in Machine Learning Pipelines

NLP and SML Open in Colab

NLP and UML Open in Colab

Bonus: FastAI SOTA Classificaiton Open in Colab

Web Mining of Firm Websites (guest lecture by Jan Kinne)

Web Mining of Firm Websites (slides)

EZ text mining Open in Colab

Portfolio work: Natural language processing

For portfolio work today, we are focusing on natural language processing.

Link to portfolio notebook: Portfolio assignments

Remember to click “Copy to Drive” to create your own copy to work with!

Requirement: Work on solutions for the “Trump vs. GPT-2” assignment.

Friday

Explainable AI (guest lecture by Thomas B. Moeslund)

Deep Learning & XAI (slides)

Evaluating Machine Learning Models

Hands-on Introduction to Explainable ML & AI Open in Colab

Methodological Outlook

This sessions is an open discussion between the instructors and the participants. The main instructors of the course will be present to answer questions, discuss ideas and suggest where to go from this course.