Künstliche Intelligenz

MachineLearnAthon

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Microlectures

Our collection of 55 Micro Lectures provides concise, focused introductions to key topics in machine learning. Each lecture is available as a PDF and a corresponding YouTube video, making it easy to learn at your own pace and revisit specific concepts whenever needed.
The lectures are organized into four thematic areas, each presented in its own table:

Basic Concepts: Fundamental principles of machine learning.
Data Preprocessing: Techniques for preparing and cleaning data before model training.
Machine Learning Frameworks: Overviews of popular frameworks for ML development.
Machine Learning Algorithms: Detailed explanations of various algorithms and their applications.

To help you quickly find what you’re looking for, we also provide a comprehensive Glossary that lists which topics are covered in which Micro Lecture — an ideal resource for students searching for specific content or revisiting particular subjects.
All videos are also available on our official YouTube channel, MachineLearnAthon. There, you’ll find four curated playlists — Getting started with Computer Vision, Getting started with Time-Series Forecasting, Getting started with Classification, and Getting started with Regression. These playlists are particularly well suited for beginners, as they guide viewers through the topics in a logical sequence designed to build understanding step by step.

Basic Concepts:

Title	PDF Link	Youtube Link
Introduction to ML	Introduction to ML	Introduction to machine learning
Classification	Classification 1 Classification 2 Classification 3	Classification I Classification II Classification III
Regression	Regression analysis quide	Regression Analysis Guide
Clustering	Clustering	Clustering
Time-Series Forecasting	Time Series Forecasting	Time-Series Forecasting
Computer Vision	MicroLecture_ComputerVision	Computer vision – Part I Computer vision – Part II Computer vision – Part III Computer vision – Part IV Computer vision – Part V Computer vision – Part VI Computer vision – Part VII
Installing Python & using it	Installing Python	Installing Python
Introduction to Git	Introduction to Git	Introduction to Git

Data Preprocessing:

Title	PDF Link	Youtube Link
Feature Selection & Engineering	Feature Engineering	Feature Engineering
Data Preparation & Visualization	Data Preparation (for tabular data)	Data preparation (for tabular data)
Hyperparameter Tuning	Hyperparameter Tuning	Hyperparameter Tuning
Evaluation Metrics	Evaluation metrics	Evaluation metrics

Machine Learning Frameworks:

Title	PDF Link	Youtube Link
Explainability & Interpretability	Interpretability	Interpretability I Interpretability II Interpretability III
Fairness in ML	Fairness in Machine Learning	Fairness in Machine Learning
Low Code No Code ML	Low code 1 Low code 2 No code	Low code I – Model creation Low code II – Model evaluation No code

Machine Learning Algorithms:

Title	PDF Link	Youtube Link
Neural Networks	Microlecture_NeuralNetworks	Neural networks – Part I Neural networks – Part II Neural networks – Part III Neural networks – Part IV Neural networks – Part V Neural networks – Part VI Neural networks – Part VII Neural networks – Part VIII
Random Forest	Random Forest	Random Forest
XGBoost	XGBoost	Introduction to XGBoost Algorithm
LightGBM	LightGBM	LightGBM
Time-Series Methods	Forecasting Methods	Forecasting Methods
Large Language Models	MicroLecture_LLM_Ethics	Large language models – Part I Large language models – Part II Large language models – Part III Large language models – Part IV Large language models – Part V Large language models – Part VI Large language models – Part VII

The creation of these resources has been
(partially) funded by the ERASMUS+ grant
program of the European Union under grant
no. 2022-1-DE01-KA220-HED-000086932.

Neither the European Commission nor the
project’s national funding agency DAAD are
responsible for the content or liable for any
losses or damage resulting of the use of
these resources.