Github Sangnguyens Introduction To Machine Learning I2ml Gh Page

This Project offers a free, open source introductory and applied overview of supervised machine learning. Main course site: https://slds-lmu.github.io/i2ml/ We are collecting ideas for future development in this list. For suggestions regarding the existing material, please open an issue. We hope to continuously improve and expand this course over the coming years. We strongly believe in open source and collaborative work.

Please contact us if you think likewise and would like to contribute. See also our contributing guidelines This work is licensed under a Creative Commons Attribution 4.0 International License. There was an error while loading. Please reload this page. 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), 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 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. We also want this course not only to be open, but open source.

This project offers a free, open source introductory and applied overview of supervised machine learning. We are collecting ideas for future development in this list. For suggestions regarding the existing material, please open an issue. We hope to continuously improve and expand this course over the coming years. We strongly believe in open source and collaborative work. Please contact us if you think likewise and would like to contribute.

See also our contributing guidelines If you use our material, please consider citing us as follows: This course is based on our concept of open-source educational resources (OSER) as described in our paper: This chapter introduces the basic concepts of Machine Learning. We focus on supervised learning, explain the difference between regression and classification, show how to evaluate and compare Machine Learning models and formalize the concept of learning. This chapter treats the supervised regression task in more detail.

We will see different loss functions for regression, how a linear regression model can be used from a Machine Learning perspective, and how to extend it with polynomials for greater flexibility. This chapter treats the supervised classification task in more detail. We will see examples of binary and multiclass classification and the differences between discriminative and generative approaches. In particular, we will address logistic regression, discriminant analysis and naive Bayes classifiers. This chapter treats the challenge of evaluating the performance of a model. We will introduce different performance measures for regression and classification tasks, explain the problem of overfitting as well as the difference between training and test error, and, lastly, present a variety of resampling techniques.

This chapter addresses \(k\)-nearest neighbors, a distance-based algorithm suited to both regression and classification. Predictions are made based upon neighboring observations, assuming feature similarity translates to target similarity. The deploy is done automatically via GitHub Actions. Nothing needs to be done to publish a new version of the website. Each build is triggered when a commit is done to the main branch. If you use our material, please consider citing us as follows:

This course is based on our concept of open-source educational resources (OSER) as described in our paper: Complete PDF of all lecture slides from chapters 1-10: Complete PDF of all lecture slides from chapters 11-19: There was an error while loading. Please reload this page. There was an error while loading.

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