Machine Learning Toolkit - One Concept • One Example

This is the second module in a series of five foundational toolkits for applied mathematics. This series follows the textbook "Applied Mathematics Toolkit: Modeling, Data, and Algorithms for Scientists and Engineers", which will be available at the end of 2026. This module covers the third chapter of the book. In each lecture we will cover one concept and one example. Feel free to contact the authors Nan Chen or myself (Charlotte Moser) via email at chennan@math.wisc.edu or char.rosemoser@gmail.com with any questions. More information about our work can be found at https://people.math.wisc.edu/~nchen29/ or https://sites.google.com/view/charlotte-moser.

Curated by: Charlotte Moser (19 videos)

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Tracks in this Playlist

• 18 - Machine Learning With Limited Data: Entropy-Constrained Learning
• 17 - Machine Learning With Limited Data: Transfer Learning
• 16 - Neural Networks: The Role of Nonlinear Activation Functions
• 15 - Neural Networks: The Role of Weights and Biases
• 14 - Neural Networks: Training a Neural Network
• 13 - Neural Networks: Combing Neurons into a Neural Network
• 12 - Neural Networks: Basic Architecture
• 11 - Gaussian Process Regression
• 10 - Comparing Principal Component Analysis and Linear Regression
• 9 - Linear Regression
• 8 - K Nearest Neighbors
• 7 - Logistic Regression: Probabilistic Classification for Binary Outcomes
• 6 - Fuzzy Clustering: Fuzzy C-means and Gaussian Mixture Models
• 5 - K-Means Algorithm in High Dimensional Cases
• 4 - Determining the Number of Clusters in the K-Means Algorithm
• 3 - K Means Algorithm: A Canonical Clustering Method
• 2 - Principal Component Analysis: Definition
• 1 - Principal Component Analysis: Motivation
• 0 - Machine Learning Toolkit Module Overview