# MICRO-570: Advanced Machine Learning

# Introduction

This compendium is made for the course CS-450 Advanced Machine Learning at École Polyteqnique Fédérale de Lausanne (EPFL) and is a summary of the lectures and lecture notes. Is is not the complete curriculum, but rather a list of reading material.

Professor: Aude Billard

Webpage: http://lasa.epfl.ch/teaching/lectures/ML_MSc_Advanced/index.php

# Course content

## Main types of learning methods

- Supervised learning
- Classification
- Regression

- Reinforcement learning
- Unsupervised learning

### Classification

Typically binary classification, which can be combine into classifying many classes. Simple example is image classification.

Learning facital expressions can be difficult if we don't preprocess the data to standarize all inputs.

Always split the data set and use one part for training and one part for testing.

Classification is also called supervised clustering process.

One problem is unbalanced classes

- Support vector machine
- Relevance vector machine
- Boosting - random projections
- Boosting - random gaussians
- Random forest
- Gaussian process

### Regression

Map N-dim. input

**Issues**:
- Influenced on choice of training points

- Support vector regression
- Relevance vector regression
- Gausiiean process regression
- Gradient boosting
- Locally weighted projected regression

# Reinforcement learning

# Unsupervised learning

Observe a series of input:

Find regularities in the inpute.g.g correlations, patterns.

Use these to reduve the dimentioality of the data.

**Pattern Recognition: Determining Trends**:
Moving time window

**Pattern Recognition: Clustering**
I.e. medical images

Clustering assume groups of points are similar. Can discover when the metric changes according to the same metric of similarity

- PCA
- Kernel PCA
- Genetic algoritms
- Kernel K-means
- Isomap
- Laplacian map
- Projections on Lapacian eigenvector