CLIMATE CHANGE|NUCLEAR POWER| RENEWABLE ENERGY|ENERGY STORAGE|GEOENGINEERING

Outline:

An introduction to the science of climate change, the physics of energy generation and the possibility of intervening in the Earth&#039;s climate....

Outline:

An introduction to the science of climate change, the physics of energy generation and the possibility of intervening in the Earth's climate. Climate change, energy science, energy production, renewable energy sources, nuclear power, electricity generation, the future of energy and climate intervention will be discussed.

Aims:

The aim of the course is to provide an introduction to the science of climate change, the physics of energy generation and distribution by various means, and the possibility of intervening in the Earth’s climate.

Teaching and Learning Methodology:

This course is delivered via weekly lectures supplemented by a series of workshops and additional discussion.

In addition to timetabled lecture hours, it is expected that students engage in self-study in order to master the material. This can take the form, for example, of practicing example questions and further reading in textbooks and online.

Indicative Topics:

The course is typically divided into eight sections. The approximate assignment of lectures to each indicative topic is shown in brackets.

Climate Change (4)
	Energy Science (3)
	Energy Production (3)
	Renewable Energy Sources (8)
	Nuclear Power (4)
	Electricity Generation, Transmission and Storage (2)
	Energy: The Future (3)
	Climate Intervention (6)

This module discusses modern methods from Computer Science and Statistics for the analysis of large datasets. Those “Machine Learning” or...

This module discusses modern methods from Computer Science and Statistics for the analysis of large datasets. Those “Machine Learning” or “Statistical Learning” methods have become essential tools for the exploration and evaluation of data for scientists and practitioners in many fields, including Economics and Finance. Various approaches for data inspection, inference and prediction will be discussed both from a theoretical and applied perspective. The course will in particular cover cluster analysis, dimensional reduction methods (like LASSO and principal components), artificial neural networks, and other related topics.