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## TODO Machine Learning
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### Notes on what goes where
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Machine learning practice code goes in a cs/machine-learning repo.
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* Code focused principally on concepts in machine learning,
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* Code uses scikit-learn and other external libraries
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* Fundamental algorithms go in the Python/respective language repo
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Repo has a copy on Github, with an HTML landing page. Or, complements circe.
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* HTML landing page with info about each topic.
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* Notebook for each overarching topic.
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* Split into multiple notebooks as needed.
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* Example might be, notebook to compare ridge and lasso.
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### TODO List
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[ ] Bayes Decision Theory
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- 05/25
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- Books:
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- Alpaydin Introduction to Machine Learning
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- Wiki notes:
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* Dimensionality Reduction
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* Regression
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* Regularization
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* Classification
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* Clustering
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* Bayesian
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* Decision Theory
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* Decision Trees
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* Association Rules
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* Neural Networks
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* Deep Learning
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- [ ] Regression: linear regression
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- [ ] Regression: logistic regression
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- [ ] Regression: OLS regression
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- [ ] Regression: Stepwise regressoin
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- [ ] Regression: MARS
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- [ ] Regression: LOESS
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- [ ] Instance: k-Nearest Neighbor
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- [ ] Instance: Learning Vector Quantization
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- [ ] Instance: Self-Organizing Map
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- [ ] Instance: Locally Weighted Learning
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- [ ] Regularization: Ridge regression
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- [ ] Regularization: LASSO
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- [ ] Regularization: Elastic net
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- [ ] Regularization: LARS
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- [ ] Decision Tree: classification tree
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- [ ] Decision Tree: CHAID
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- [ ] Decision Tree: conditional decision trees
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- [ ] Bayesian: LARS
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- [ ] Bayesian: Naive Bayes
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- [ ] Bayesian: Gaussian Bayes
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- [ ] Bayesian: Multinomial Naive Bayes
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- [ ] Bayesian: Bayesian Network
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- [ ] Bayesian: Bayesian Belief Network
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- [ ] Clustering: k-Means
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- [ ] Clustering: k-Medians
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- [ ] Clustering: Expectation Maximization
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- [ ] Clustering: Hierarchical Clustering
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- [ ] Dimensionality Reduction: PCA
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- [ ] Dimensionality Reduction: t-SNE
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- [ ] Dimensionality Reduction: PLS
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- [ ] Dimensionality Reduction: Multidimensional Scaling
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- [ ] Dimensionality Reduction: Principal Component Regression
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- [ ] Dimensionality Reduction: Discriminant Analyses
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- [ ] Association Rule: Apriori algorithm
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- [ ] Deep Learning: CNN
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- [ ] Deep Learning: RNN
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- [ ] Deep Learning: LSTM
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- [ ] Deep Learning: DBM
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- [ ] Deep Learning: DBN
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- [ ] Deep Learning: Stacked Auto-Encoders
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