Image classification|object detection|simultaneous localisation and mapping|path planning.

The course provides a general understanding of machine learning algorithms which are closely related to robotics, for e.g. machine vision and path planning....

Machine Learning for Robotics (ELEC0144)

The course provides a general understanding of machine learning algorithms which are closely related to robotics, for e.g. machine vision and path planning. The content / syllabus of the module includes image processing, neural networks, convolutional neural networks, deep learning, image classification, object detection, SLAM (simultaneous localization and mapping), and path planning.

Module Content and Indicative Topics

Since its &#039;invention&#039; in the nineteenth century, detective fiction (or more broadly, crime fiction) has grown to...

This module will cover applications of machine learning (ML) methods and other high-dimensional estimation methods for causal inference in economics. While ML...

Machine Learning in Economics (ECON0126)

This module will cover applications of machine learning (ML) methods and other high-dimensional estimation methods for causal inference in economics. While ML are generally designed for prediction, causal inference generally evolves around parameter estimation and statistical inference. The module will present methods that use ML methods for causal inference. The applications include Instrumental Variable models, treatment effects, policy evaluation, counterfactual analysis, estimation of structural economic models, panel data models.

 

Network science is a novel transdisciplinary approach to the study of complex systems. It integrates theoretical results with empirical “big data”...

Network Science for Economics (ECON0123)

Aims: To provide students with the analytical skills needed to assess the main determinants of the prices and returns of any asset or investment.  By the end...

Advanced Economics of Finance (ECON0113)

Aims: To provide students with the analytical skills needed to assess the main determinants of the prices and returns of any asset or investment.  By the end of the module, you will be able to see how finance fits within economics and use your general economic knowledge applied to real life investment decisions. It should serve as a solid base for graduate studies in the subject or for pursuing a career in finance.



Suitable for: Final year BSc (Econ) Economics (L100, L101 and L102) students; BSc Economics and Geography (UBEECOAGEO05), BA Philosophy and Economics (UBAPHIAECO09), BSc Politics, Philosophy and Economics PPE (UBSPPESING05) and BA European Social and Political Studies (UBABAESPSTU05) students. Students from other degree programmes can also take the module if they meet the prerequisite requirements.



Prerequisites: ECON0013: Microeconomics or a suitable alternative that meets the requirements in the “Assumed Knowledge” section. Students are encouraged to take the Level 5 module ECON0048: Economics of Finance, in their second year.



Assumed knowledge: You should be very familiar with general equilibrium using a mathematical approach (marginal utility, constrained consumer choice and marginal rates of substitution). Because uncertainty plays a key role you must have a firm understanding of statistics. You must not only be able to manipulate and calculate an expectation, variance and covariance, but have good intuition for each of these concepts as they play a key role: almost every problem/equation/equilibrium condition in this course involves expectation/covariance terms as key components.