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# Data Science III (course at WHZ)

Part three of the data science lecture series continues discussion of supervised machine learning. Further methods like decision trees and support vector machines are introduced. Then we move on to unsupervised machine learning covering clustering methods and techniques for dimensionality reduction.

## Supervised Learning

### Week 1 (Decision Trees)

Lectures

- Decision Trees

  - Basics

  - Regression Trees

  - Classification Trees

Self-study

- IBAN recognition (project)

Practice session

- Forged Banknotes

  - Decision Tree (project)

### Week 2 (Ensemble Methods)

Lectures

- Ensemble Methods

  - The Idea

  - Stacking

  - Bagging

  - Boosting

Self-study

- revisit Worked Example: House Prices II

Practice session

- Forged Banknotes

  - Random Forest (project)

- House Prices

  - A Random Forest for House Prices (project)

### Week 3 (Support-Vector Machines)

Lectures

- Support-Vector Machines

  - Hard Margin SVMs

  - Soft Margin SVMs

  - Kernel SVMs

  - SVMs with Scikit-Learn

Self-study

- Support-Vector Machines

  - Support-Vector Regression (SVR) (bonus)

Practice session

- Forged Banknotes

  - Support-Vector Machine (project)

- MNIST Character Recognition

  - SVM for QMNIST (project)

### Week 4 (Naive Bayes)

Lectures

- Naive Bayes Classification (without kernel density estimates)

Self-study

- Naive Bayes Classification (kernel density estimates, bonus)

Practice session

- Forged Banknotes

  - Naive Bayes Classification (project)

### Week 5 (Text Classification)

Lectures

- Text Classification

  - Preprocessing Text Data

  - Training

Self-study

Practice session

### Week 6 (Text Classification)

Lectures

Self-study

Practice session

- Blogs

  - Blog Author Classification (Training) (project)

  - Blog Author Classification (Test) (project)

## Unsupervised Learning

### Week 7 (Introduction, Centroid-based Clustering)

Lectures

- Typical Tasks

- Quality Measures for Clustering

- Centroid-based Clustering (k-Means)

Self-study

Practice session

- MNIST Character Recognition

  - Semisupervised Classification (project)

### Week 8 (Hierarchical Clustering)

Lectures

- Hierarchical Clustering

Self-study

Practice session

- Supermarket Customers (project)

- Chinese Celadons

  - Hierarchical Clustering (project)

### Week 9 (Density-based Distribution-based Clustering)

Lectures

- Density-based Clustering

  - DBSCAN

  - OPTICS

- Distribution-based Clustering

Self-study

Practice session

- Chinese Celadons

  - Density-based Clustering (project)

- MNIST Character Recognition

  - Generating Handwritten Digits (project)

### Week 10 (Autoencoders)

Lectures

- Autoencoders

Self-study

Practice session

- MNIST Character Recognition

  - Autoencoder for QMNIST (project)

### Week 11 (Nonlinear Dimensionality Reduction, Kernel PCA)

Lectures

- Nonlinear Dimensionality Reduction Overview

- Kernel PCA

Self-study

- Feature Reduction (reread PCA section)

Practice session

### Week 12 (Multidimensional Scaling)

Lectures

- Multidimensional Scaling

Self-study

- Feature Reduction (reread PCA section)

Practice session

- Color Perception (project)

- Forest Fires (project)

### Week 13 (LLE, SNE, SOM)

Lectures

- Locally Linear Embedding

- Stochastic Neighbor Embedding

- Self-Organizing Maps

Self-study

Practice session

- MNIST Character Recognition

  - t-SNE for QMNIST (project)

- House Prices

  - House Prices SOM (project)