Machine Learning: The elegant way to extract information from data

Machine Learning: The elegant way to extract information from data

Drs Jens Kauffmann, Veselina Kalinova, Dario Colombo and Tobias Albertson (MPIfR)

Overview

The human brain is able to recognize patterns in the data due to the millions of years of evolution. Today, we try to use and further develop computer algorithms that can reproduce this ability to distinguish patterns in large statistical samples and images, to find correlations between events, to classify objects based on their similar and different features in an objective and automatic way. Machine Learning is a sub-field in computer science that explores pattern recognition in the data analysis and characterize already known trends in sets of observations. This course focuses on the basic most applied up-to-date techniques in the machine learning that can be used in any branch of science and industry. We start with a broad introduction about the topic, followed by detailed discussion about the theory and application of the different types of the machine learning — supervised and unsupervised learning. In the field of supervised learning, for example, we will discuss regression, support vector machines, and neural networks, while the field of unsupervised learning we will cover clustering techniques, principal component analysis, and dimensionality reduction. The final goal of the course is to give the hands–on knowledge needed to pick and apply machine learning tools in Python. Discussions of the underlying mathematical principles will illustrate the inner workings of these tools.

Topics to be covered

Introduction — what is machine learning?
Supervised Learning — regression, support vector machines, neural networks
Unsupervised Learning — clustering, principal component analysis, dimensionality reduction
Tools — Monte Carlo Markov chains, Bayesian inference

 

Schedule

Lecture 1: February 13 at 10:00 in 0.02: Introduction to Machine Learning: data mining, pattern recognition, dimensionality reduction, validation, objectivism | .mov  .mp4

Lecture 2: February 14 at 10:00 in 0.02: Principal Component Analysis (PCA): eigenvectors, eigenvalues, variance, covariance, eigenfaces, application to astronomy | .mov  .mp4

Lecture 3: February 15 at 10:00 in 0.02: Clustering I: distance definitions, hierarchical clustering, k-means | .mov  .mp4

Lecture 4: February 16 at 10:00 in 0.02: Clustering II: graph theory, spectral clustering, DBSCAN, expectation maximization via gaussian mixture models, affinity propagation | .mov  .mp4

Lecture 5: February 20 at 10:00 in 0.02: Regression: chi-squared fitting, Markov Chain Monte Carlo (MCMC), Bayesian inference | .mov  .mp4

Lecture 6: February 21 at 10:00 in 0.02: Classification I: support vector machines, perceptron, artificial neural networks | .mov  .mp4

Lecture 7: February 22 at 10:00 in 0.02: Classification II: decision trees, naive Bayes, genetic algorithm | .mov  .mp4

Lecture 8: February 23 at 10:00 in 0.02: Summary: review of methods, validation,connections to astronomy, resources for further learning | .mov  .mp4