Name		Name	Last commit message	Last commit date
Latest commit History 32 Commits
data		data
.gitignore		.gitignore
Lecture 04 - Exploratory Data Analysis.ipynb		Lecture 04 - Exploratory Data Analysis.ipynb
Lecture 05 - Maximum Likelihood Estimates.ipynb		Lecture 05 - Maximum Likelihood Estimates.ipynb
Lecture 07 - More on Bayesian Inference.ipynb		Lecture 07 - More on Bayesian Inference.ipynb
Lecture 09 - Introduction to Unsupervised Learning.ipynb		Lecture 09 - Introduction to Unsupervised Learning.ipynb
Lecture 11 - Dimensionality Reduction and Manifold Learning.ipynb		Lecture 11 - Dimensionality Reduction and Manifold Learning.ipynb
Lecture 14 - More on Clustering.ipynb		Lecture 14 - More on Clustering.ipynb
Lecture 15 - Linear Regression.ipynb		Lecture 15 - Linear Regression.ipynb
Lecture 17 - Training, Validation and Testing Sets. Residual Analysis.ipynb		Lecture 17 - Training, Validation and Testing Sets. Residual Analysis.ipynb
Lecture 18 - Data Requirements for Linear Regression.ipynb		Lecture 18 - Data Requirements for Linear Regression.ipynb
Lecture 19 - More Linear Models.ipynb		Lecture 19 - More Linear Models.ipynb
Lecture 21 - Classification.ipynb		Lecture 21 - Classification.ipynb
Lecture 22 - Support Vector Machines.ipynb		Lecture 22 - Support Vector Machines.ipynb
Lecture 23 - Classification Trees.ipynb		Lecture 23 - Classification Trees.ipynb
Lecture 24 - Ensemble Methods.ipynb		Lecture 24 - Ensemble Methods.ipynb
Lecture 25 - Class Imbalance.ipynb		Lecture 25 - Class Imbalance.ipynb
Lecture 26 - Multiclass Classification.ipynb		Lecture 26 - Multiclass Classification.ipynb
Lecture 36 - High Dimensional Data, Autocorrelation and Basic Image Analysis.ipynb		Lecture 36 - High Dimensional Data, Autocorrelation and Basic Image Analysis.ipynb
README.md		README.md
requirements.txt		requirements.txt

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