Introduction to Machine Learning and Data Analysis

Learning outcomes:

Overview of machine learning pipelines and their implementation with scikit-learn
Regression and Classification: linear models and logistic regression
Decision trees & random forest models
Clustering with K-means and Gaussian mixtures
Principal component analysis (PCA) and non-linear embeddings (t-SNE and UMAP)
Artificial Neural networks as general fitters, fully connected nets used to classify the fashion-MNIST dataset
Scikit-learn and clustering maps, Q&A

Our wepgabe is dsl.unibe.ch

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additional_material		additional_material
data		data
figures		figures
prep		prep
utils		utils
whiteboards		whiteboards
.gitignore		.gitignore
Course_1A.ipynb		Course_1A.ipynb
Course_1B.ipynb		Course_1B.ipynb
Course_2.ipynb		Course_2.ipynb
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Course_4.ipynb		Course_4.ipynb
LICENSE		LICENSE
Project_guidelines.pdf		Project_guidelines.pdf
Python_refresher.ipynb		Python_refresher.ipynb
README.md		README.md
Solutions_1-AB.ipynb		Solutions_1-AB.ipynb
Solutions_2.ipynb		Solutions_2.ipynb
Solutions_3.ipynb		Solutions_3.ipynb
Solutions_4.ipynb		Solutions_4.ipynb
colab_material.tgz		colab_material.tgz
make_colab_material.sh		make_colab_material.sh

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