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