| title | Home |
|---|---|
| permalink | / |
Introduction to Machine Learning
Welcome to the landing page of the coursework entitled "Introduction to Machine Learning". This course will build further on the handbook "An Introduction to Statistical Learning with Applications in R" written by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. The handbook, R code, slides and much more material can be downloaded freely at https://www.statlearning.com/.
This course will use the handbook as backbone to provide deep-dive content, such as video presentations, jupyter notebooks and some mathematical underpinning to important concepts that were only briefly introduced in the handbook. But before running comes walking. Therefore it is good to revise the material in the handbook by watching the video lectures of Trevor Hastie and Robert Tibshirani. For those who need more training in R, please refer to the home page of Abbass Al Sharif at https://www.alsharif.info/iom530.
The lab sessions at the end of each session can be viewed as static webpage and Jupyter notebooks are available for download.
The powerpoint presentation for lecturing and workshops can be found overhere.
We included some example exam questions to help guide you through the chapters. Keep these questions in mind during studying, it will help you to understand the material. If you struggle with some questions, look what fellow students already have done for you: https://github.com/asadoughi/stat-learning.