AI methods
Course Overview
Explore the intricacies of machine learning models and datasets in this comprehensive course. Begin by understanding benchmark datasets, focusing on the air quality benchmark dataset AQ-Bench. Delve into how neural networks and shallow neural networks represent training data, requiring basic knowledge of activation functions. Expand your expertise by exploring how random forests, built on decision trees, handle training data. Acquire a valuable tool to analyze prediction quality in relation to error sources within your model and dataset. Synthesize your learning by combining explanations of machine learning models with the AQ-Bench dataset and insights on inaccurate predictions using k nearest neighbors. Master essential concepts, ensuring a holistic understanding of machine learning model representation and dataset analysis.
Details
Lecturer
Overview
Explore the intricacies of machine learning models and datasets in this comprehensive course. Begin by understanding benchmark datasets, focusing on the air quality benchmark dataset AQ-Bench. Delve into how neural networks and shallow neural networks represent training data, requiring basic knowledge of activation functions. Expand your expertise by exploring how random forests, built on decision trees, handle training data. Acquire a valuable tool to analyze prediction quality in relation to error sources within your model and dataset. Synthesize your learning by combining explanations of machine learning models with the AQ-Bench dataset and insights on inaccurate predictions using k nearest neighbors. Master essential concepts, ensuring a holistic understanding of machine learning model representation and dataset analysis.
The global air quality Benchmark dataset AQ-Bench
In this nugget you will learn about the air quality benchmark dataset AQ-Bench. To understand this nugget, it is helpful if you have an idea of a benchmark dataset.
Shallow Neural Network weight visualization
In this nugget, you will discover how neural networks represent the training data. To understand this nugget, you should know the basics about shallow neural networks and activation functions.
The leaf activation method for the Random Forest
In this nugget, you will discover how random forests represent the training data. To understand this nugget, you should know decision trees and the random forest algorithm.
Explaining inaccurate predictions using nearest neighbors
In this nugget, you will acquire a tool to relate the quality of your predictions to error sources in your model and your dataset.
Results on capabilities and limitations of AQ-Bench
In this nugget, we combine the explanations of our machine learning models with the dataset we used to train them. This nugget synthesizes the nugget on the AQ-Bench dataset and the nugget on explaining inaccurate predictions with k nearest neighbors.
