Machine-learning-notebooks

9 notebooks made as preparation for machine learning oral exam at AU, and for future reference.

1: Linear models

2: Learning theory

3: Support vector machines

4: Neural networks

5: Decision trees & ensemble methods

6: Hidden markov models - Decoding

7: Hidden markov models - Training

8: Unsupervised learning - Clustering

9: Unsupervised learning - Outlier detection

Video resources:

Name		Name	Last commit message	Last commit date
Latest commit History 54 Commits
imgs		imgs
.gitignore		.gitignore
1LinearModels.ipynb		1LinearModels.ipynb
2LearningTheory.ipynb		2LearningTheory.ipynb
3SVM.ipynb		3SVM.ipynb
4NeuralNetworks.ipynb		4NeuralNetworks.ipynb
5DecisionTreesEnsembleMethods.ipynb		5DecisionTreesEnsembleMethods.ipynb
6HmmDecoding.ipynb		6HmmDecoding.ipynb
7HmmTraining.ipynb		7HmmTraining.ipynb
8UnsupervisedClustering.ipynb		8UnsupervisedClustering.ipynb
9UnsupervisedOutlierDetection.ipynb		9UnsupervisedOutlierDetection.ipynb
README.md		README.md

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