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 scits.unibe.ch

Name		Name	Last commit message	Last commit date
Latest commit History 166 Commits
binder		binder
data		data
figures		figures
prep		prep
utils		utils
.gitignore		.gitignore
Course_1.ipynb		Course_1.ipynb
Course_2.ipynb		Course_2.ipynb
Course_2_venice.ipynb		Course_2_venice.ipynb
Course_3.ipynb		Course_3.ipynb
Course_3_venice.ipynb		Course_3_venice.ipynb
Course_4.ipynb		Course_4.ipynb
LICENSE		LICENSE
Project_indications.pdf		Project_indications.pdf
Python_key_points_homework.ipynb		Python_key_points_homework.ipynb
README.md		README.md
Solutions_1.ipynb		Solutions_1.ipynb
Solutions_2.ipynb		Solutions_2.ipynb
Solutions_2_venice.ipynb		Solutions_2_venice.ipynb
Solutions_3.ipynb		Solutions_3.ipynb
Solutions_3_venice.ipynb		Solutions_3_venice.ipynb
Solutions_4.ipynb		Solutions_4.ipynb
colab_material.tgz		colab_material.tgz
handout.pdf		handout.pdf
handout_with_solutions.pdf		handout_with_solutions.pdf

Provide feedback