Machine Learning is the science and art of extracting meaningful information from data. In supervised machine learning, we use a training set of annotated examples to teach a computer to make valid predictions, or to reliably recognize items. In unsupervised learning, we use computers in an exploratory fashion, to discover interesting patterns in data.
Machine learning and pattern recognition are currently taking center stage in applications ranging from autonomous driving to social network analysis and drug development.
This seminar covers the essential techniques typically taught in an introductory lecture, including
- statistical learning theory
- generative and discriminative classifiers
- ridge regression, lasso
- logistic regression, generalized linear models
- kernel methods, support vector machine
- perceptron, multi-layer perceptron, neural networks
- dimension reduction
- cluster analysis
Modalities
On Tuesday, April18th 2017, I will give general guidelines on "how to give a talk", and will outline the different topics. To choose a topic, you must be present on that occasion.
If you join, you will conduct a literature search on your topic and give a 45 min talk. You will receive 2 ECTS points and a grade based on: content of your talk (1/2) and presentation (1/2). This is a "Pflichtseminar" that is eligible towards the BSc in Physics.
The seminar has been heavily oversubscribed in past semesters. If you wish to participate, please send an email to lab manager Barbara Werner mailto:hamprecht-lab-manager@iwr.uni-heidelberg.de?subject=ML-Seminar-Re.... The first student to register gets first choice amongst the topics, second student second choice and so on.
Venue
Tuesdays at 11:15, in seminar room 10 of Mathematikon, INF 205. We start on time.