Update _index.md

content/_index.md

@@ -4,12 +4,12 @@ title: Introduction to Machine Learning (I2ML)
 
 This website offers an open and free introductory course on (supervised) machine learning. The course is constructed as self-contained as possible, and enables self-study through lecture videos, PDF slides, cheatsheets, quizzes, exercises (with solutions), and notebooks.
 
-The quite extensive material can roughly be divided into an introductory undergraduate part (chapters 1-10) and a more advanced second one on MSc level (chapters 11-20). At the [LMU Munich](https://www.slds.stat.uni-muenchen.de/teaching/) we teach both parts in an inverted-classroom style (B.Sc. lecture "Introduction to ML" and M.Sc. lecture "Supervised Learning"). While the first part aims at a practical and operational understanding of concepts, the second part discusses focuses on theoretical foundations and more complex algorithms. 
+The quite extensive material can roughly be divided into an introductory undergraduate part (chapters 1-10), a more advanced second one on MSc level (chapters 11-19), and a third course, on MSc level (chapters 20-23). At the [LMU Munich](https://www.slds.stat.uni-muenchen.de/teaching/) we teach all parts in an inverted-classroom style (B.Sc. lecture "Introduction to ML" and M.Sc. lectures "Supervised Learning" and "Advanced Machine Learning"). While the first part aims at a practical and operational understanding of concepts, the second and third parts focus on theoretical foundations and more complex algorithms. 
 
 __Remarks on deep-Dive sections__: Certain sections exclusively present mathematical proofs, acting as deep-dives into the respective topics. It's important to note that these deep-dive sections do not have accompanying videos.
 
-__Why another ML course:__ A key goal of the course is to teach the fundamental building blocks behind ML, instead of introducing “yet another algorithm with yet another name”. We discuss, compare and contrast risk minimization, statistical parameter estimation, the Bayesian viewpoint and information theory and demonstrate that all of these are equally valid entry points to ML. Developing the ability to take on and switch between these perspectives is a major goal of this course, and in our opinion not always ideally presented in other courses. 
+__Why another ML course:__ A key goal of the course is to teach the fundamental building blocks behind ML, instead of introducing “yet another algorithm with yet another name”. We discuss, compare, and contrast risk minimization, statistical parameter estimation, the Bayesian viewpoint, and information theory and demonstrate that all of these are equally valid entry points to ML. Developing the ability to take on and switch between these perspectives is a major goal of this course, and in our opinion not always ideally presented in other courses. 
 
-We also want this course not only to be open, but [open source](https://arxiv.org/pdf/2107.14330.pdf). 
+We also want this course not only to be open, but [open source](https://proceedings.mlr.press/v207/bothmann23a.html). 
 
 __What is not covered:__ (1) An in-depth coverage of deep learning, we offer this in our course [Introduction to Deep Learning](https://slds-lmu.github.io/i2dl/). (2) An in-depth coverage of optimization - we are working on a separate course for optimization.