Course AI Campus Original

Introduction to Machine Learning Part 2: Algorithms

The second part of the course Introduction to Machine Learning

The course image for I2ML Part 2
📊︎ Intermediate
40 hours
🏅︎ Record of Achievement
🎁︎ For free
4 (3)
© CC BY-SA 4.0
🌐︎ English
This course belongs to the course series Introduction to Machine Learning

Overview

Machine Learning (ML) is at the core of many applications of artificial intelligence. A key goal of this course series is to teach the fundamental building blocks behind supervised ML. In this second part, we will present to you a variety of machine learning algorithms such as k-nearest neighbors, classification and regression trees, random forests, and neural networks.

Which topics will be covered?

  • Theoretical understanding of different ML algorithms such as k-nearest neighbors, Classification and Regression Trees, Random Forests, and Neural Networks
  • Advantages and disadvantages of the different learners
  • Application of the learned algorithms in R and Python

What will I achieve?

  • Explain the idea of k-NN
  • Explain the idea of classification and regression trees and how random forests improve this method
  • Explain how a neural network works
  • Apply the learned ML algorithms to real-world data using R and Python

Which prerequisites do I need to fulfill?

This course is open to all who are interested. However, we recommend learners to have:

  • A strong foundation in mathematics, such as 8 years of math education in secondary schools
  • Pre-knowledge in linear algebra and analysis required (at least high school level)
  • Pre-knowledge in statistics and probability is recommended (at least high school level)
  • Basic programming skills in R or Python (e.g., through a small self-study course)
  • You have concluded the course Introduction to Machine Learning Part 1

This course is offered by

mcml logo
Bernd Bischl neu
Prof. Dr. Bernd Bischl
Ludwig-Maximilians-Universität München - Institut für Statistik
Munich Center for Machine Learning
Visit Prof. Dr. Bernd Bischl homepage (opens in a new tab)
Ludwig_Bothmann
Dr. Ludwig Bothmann
Ludwig-Maximilians-Universität München - Institut für Statistik
Munich Center for Machine Learning
Visit Dr. Ludwig Bothmann homepage (opens in a new tab)

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