Introduction to the Course
This presentation gives you advice on how to study in this course and informs you about the material available.
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Introduction to Machine Learning
This presentation is an introduction to machine learning. It categorizes machine learning by applications as well as by type of algorithm.
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NEW: Introduction to R and Positron
This presentation introduces the basics of R and using the IDE Positron (with interactive code).
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Introduction to R and RStudio - Part 2
This presentation extends part 1 and introduces functionality from the `tidyverse` package.
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k Nearest Neighbors
This presentation introduces k Nearest Neighbors using a wine dataset to predict red and white wines and using Optical Character Recognition to read digits from 0 - 9.
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Key Machine Learning Concepts - Explained with Linear Regression
This presentation introduces key machine learning concepts. It uses linear regression instead of a more advanced machine learning model to keep the model as simple as possible to focus on the key concepts of machine learning.
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Overlearning and (Cross) Validation - Explained with Polynomial Regression
This presentation shows how overlearning occurs and why it is a problem. It also shows how optimal hyper-parameters can be determined with (cross) validation to avoid overlearning.
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Regularization with Ridge and Lasso
This presentation shows how Ridge and Lasso penalties can be used for regularization.
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Logistic Regression with Unbalanced Data
This presentation introduces the classification algorithm of Logistic Regression. It also covers how to work with unbalanced datasets.
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Neural Networks
This presentation introduces Neural Networks and how the hyper-parameters of a Neural Network can be tuned to avoid overlearning.
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Tree Based - Decision Tree
This presentation introduces a tree-based model: Decision Tree
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Tree Based - Random Forest
This presentation introduces a tree-based model: Random Fores
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Interpretation of Machine Learning Models
This presentation important techniques to interpret machine learning models.
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