Final Review

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# Final Review
## AU STAT627
### Emil Hvitfeldt
### 2021-04-20

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.footnote[By @ChelseaParlett]

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# Unsupervised Learning

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# dimensionality reduction
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# Clustering
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# Dimensionality Reduction

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We looked at

- Principle Component Analysis
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Other methods to consider

- t-distributed Stochastic Neighbor Embedding (t-SNE)
- Autoencoder
- UMAP
- NNMF

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# Dimensionality Reduction

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We looked at

- Principle Component Analysis
- **Lasso**
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Not just useful in linear models
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# Dimensionality Reduction

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We looked at

- Principle Component Analysis
- Lasso
- **LDA**
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]

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# Clustering

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We looked at

- K-means
- Hierarchical Clustering
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Other models

- DBSCAN
- Gaussian Mixtures
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# Regularisation Methods

- Ridge
- Lasso

Serves different tasks. Can be combined in some cases

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# Supervised Learning

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# Regression
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# Classification
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# Supervised Learning

Many of the methods we looked at in this class can be used for both regression and classification

We mainly work with 2 types of trade-offs

- Flexibility / Interpretability
- Bias / Variance

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# Supervised Learning

The models we saw in this class lays the foundation for most models which doesn't go under the neural network umbrella

- xgboost
- lightgbm
- catboost
- stacking

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# Other considerations

- Implementation and run time
- What metrics are important
- Your problem statement

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# Thank you!