Machine Learning Free Course at Univ Wisconsin Madison

In this post, you will learn about the free course on machine learning (STAT 451) recently taught at University of Wisconsin-Madison by Dr. Sebastian Raschka. Dr. Sebastian Raschka in currently working as an assistant Professor of Statistics at the University of Wisconsin-Madison while focusing on deep learning and machine learning research.

The course is titled as “Introduction to Machine Learning”. The recording of the course lectures can be found on the page – Introduction to machine learning.

The course covers some of the following topics:

• What is machine learning?
• Nearest neighbour methods
• Computational foundation
  • Python Programming (concepts)
  • Machine learning in Scikit-learn
• Tree-based methods
  • Decision trees
  • Ensemble methods
• Model evaluation techniques
  • Concepts of overfitting & underfitting
  • Resampling methods
  • Cross-validation methods
  • Statistical tests & algorithm selection
  • Evaluation metrics

By far, these are one of the best lectures on machine learning, I have come across on the internet. You can find some other useful links such as the following in relation to Dr. Sebastian Raschka 

• Books such as Python Machine Learning (3rd Edition)
• Deep learning lectures
• A collection of mathematics resources which may prove to be useful while learning machine learning
• Python notebooks for machine learning
• Some great reads (blogs) on varied topics of machine learning. Here are my favorites
  • Model evaluation, model selection, and algorithm selection in machine learning Part I – The basics
  • Model evaluation, model selection, and algorithm selection in machine learning Part II – Bootstrapping and uncertainties
  • Model evaluation, model selection, and algorithm selection in machine learning Part III – Cross-validation and hyperparameter tuning