Gaussian Process School 2013
Kampala, Uganda
The last few years have witnessed an explosion in the quantity and variety of data available in Africa, produced either as a by-product of digital services, from sensors or measuring devices, satellites and from many other sources. A number of practical fields have been transformed by the ability to collect large volumes of data: for example, bioinformatics with the development of high throughput sequencing technology capable of measuring gene expression in cells, or agriculture with the widespread availability of high quality remote sensing data. For other data sources – such as mobile phone usage records from telecoms operators, which can be used to measure population movement and economic activity – we are just beginning to understand the practical possibilities.
Data science seeks to exploit advances in machine learning and statistics to make sense of the growing amounts of data available from various sources. In Africa, a number of problems in areas such as healthcare, agriculture, disaster response and wildlife conservation would benefit greatly if domain experts were exposed to data science techniques. These skills would allow practitioners to extract useful information from these abundant sources of raw data.
In the tradition of previous Africa Data Science workshops, a summer school on machine learning and data science will be held prior to the main workshop. This summer school will target graduate students, researchers and professionals working with huge amounts of data or unique datasets.
The summer school will focus on introductory and advanced lectures in data science and machine learning as well as moderate to advanced practical and tutorial sessions where participants will get their hands wet wrangling and munging datasets and applying cutting edge machine learning techniques to derive inference from the data. Lectures will be given by distinguished world renown researchers and practitioners including researchers from Sheffield University, IBM Research, Facebook, Pulse Lab Kampala and the AI and Data Science (AIR) lab-Makerere University.
The school will also involve end-to-end tutorial sessions from professionals walking the participants through a real data analytics problem from data acquisition to data presentation. To benefit from this course participants are encouraged to have some background in programming particularly programming with Python.
School programme outline:
Time |
Activity |
Material |
|---|---|---|
09:30-10:45 |
Linear Regression |
PDF slides video |
11:15-12:30 |
Lab Class |
PDF slides ipynb video |
Time |
Activity |
Material |
|---|---|---|
09:30-10:30 |
Basis Functions |
PDF slides video |
10:30-11:00 |
Lab Class |
PDF slides |
11:30-12:30 |
Model Selection and Bayesian Inference |
PDF slides video |
Time |
Activity |
Material |
|---|---|---|
09:30-10:15 |
Gaussian Processes I |
PDF slides video |
10:15-11:00 |
Lab Class |
PDF slides ipynb |
11:30-12:30 |
Gaussian Processes II |
PDF slides video |
14:00 |
Bring Your Own Data |
The summer school is now fully subscribed, and registration has closed.
Collaborators
Sponsorship
Sponsoring Gaussian Process School 2013 Event is a great way to communicate your commitment to support the achievement of the sustainable development goals. If youare interested in sponsoring contact info@datascienceafrica.org.