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Machine Learning Class Notes
Tamara Veenstra and Joanna Bieri
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Contents
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Front Matter
1
k Nearest Neighbors
kNN for Classification
kNN for Regression
Simple Implementation of kNN
Visualizing Decision Boundaries
Implementation of kNN on Iris Data Set
2
Dimensionality Reduction and PCA
Introduction
Visualization
Principal Component Analysis Description
PCA Implementation
PCA Variations and Caveats
3
Linear Regression
Introduction
The Cost function
Matrix Form
Minimizing the cost function
Gradient Descent
Implementation
4
Logistic Regression
Steps of Linear Regression
Data
Learn Function
Choose a Model.
Cost Function
Solve for \(\Theta\)
Make Predictions
Steps for Logistic Regression
Understanding \(\Theta\)
Logistic Regression Implementation
Multiclass logistic regression
Notes from Exam 1
5
Polynomial Regression
Introduction
Implementation
Underfitting and Overfitting
6
Parameter Tuning
Regularization
Parameter Searching
Evaluating Classification Models
7
Support Vector Machines
Introduction
Revising the cost function.
Linear Implementation
Kernel Trick
8
Clustering
K-means Clustering
Optimizing KMeans
9
Neural Networks
Artificial Neurons
Perceptrons
Multi-Layer Perceptrons
Back Propagation
Keras and Tensorflow
Hyperparameter Tuning
Convolutional Neural Network
10
Ethics and Machine Learning
Discussion of articles
Weapons of Math Destruction
Is data the new oil?
Encoding Bias
Data Collection and Privacy
Potential for Misuse of ML algorithms
Environmental Costs
Enriching Big Data
Recommended References
Authored in PreTeXt
Machine Learning Class Notes
Tamara Veenstra and Joanna Bieri
University of Redlands
tamara_veenstra@redlands.edu
