Teaching Materials Access the slides for each chapter of Guide to Intelligent Data Science to build your own curriculum. A sample curriculum is outlined below.

Disclaimer: Except otherwise noted, the teaching materials (including workflow examples, code examples, and slides) are available under the Creative Commons Attribution-NonCommercial 4.0 International Public License (CC BY-NC 4.0).

Sample Curriculum:
Machine Learning and AI for Data ScienceUniversity of Konstanz, Winter 2020/2021

Week Type Topic Notes Material
1 Lesson Introduction to Data Science Slides
Exercise KNIME Overview Install KNIME Analytics Platform Slides
Download KNIME
2 Lesson Principles of Modeling Slides
Exercise Principles of Modeling Exercises
3 Lesson Clustering Slides
Exercise Clustering Theoretical
Practical
4 Lesson Regression / Logistic Regression Slides
Exercise Regression / Logistic Regression Exercises
5 Lesson Version Space
Exercise
6 Lesson Kernel Methods and SVM Slides
Exercise SVM Theoretical
Practical
7 Lesson Decision Trees Slides
Exercise Decision Trees Theoretical
Practical
8 Lesson Classic Neural Networks Slides
Exercise Neural Networks Theoretical
Practical
9 Lesson Deep Learning incl. applications to images Slides
Exercise Deep Learning LSTM
CNN
10 Lesson Ensembles / Naïve Bayes Ensemble Methods
Bayes Classifiers
Exercise Ensembles / Naïve Bayes Ensembles Theoretical
Ensembles Practical
Bayes Classifiers Theoretical
11 Lesson Deployment, REST, Guided Analytics Slides
Exercise Exam for KNIME Certification Program Contact academia@knime.com for more information
12 Lesson Guest Lecture Phil Winters of ciagenda.com
Contact academia@knime.com for information
Exercise Q&A for written exam