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Machine Learning Notes - 2022 Fall
References
1. Introduction
1.1. What is Machine Learning?
1.2. Basic setting for Machine learning problems
1.3. Python quick guide
1.4. Exercises
2. k-NN
2.1. k-Nearest Neighbors Algorithm (k-NN)
2.2. k-NN Project 1:
iris
Classification
2.3. k-NN Project 2: Dating Classification
2.4. k-NN Project 3:
MNIST
Handwritten recognition
2.5. Exercises and Projects
3. Decision Tree
3.1. Gini impurity
3.2. CART Algorithms
3.3. Decision Tree Project 1: the
iris
dataset
3.4. Decision Tree Project 2:
make_moons
dataset
3.5. Exercises and Projects
4. Ensemble methods
4.1. Voting machine
4.2. Bootstrap aggregating
4.3.
AdaBoost
4.4. Exercises and Projects
5. Logistic Regression
5.1. Basic idea
5.2. Regularization
5.3. Neural network implement of Logistic regression
5.4. Multi class case
5.5. Exercises and Projects
6. Neural Network
6.1. Neural network: Back propagation
6.2. Example
6.3. Exercises and Projects
7. Appendix
7.1. Datasets
.md
.pdf
Appendix :numbering-format:
.. appendix
7.
Appendix :numbering-format:
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appendix