Module ECON41H15: Machine Learning

Department: Economics

ECON41H15: Machine Learning

Prerequisites

• None

Corequisites

• None

Excluded Combination of Modules

• None

Aims

• This module is an introduction to machine learning. The methods presented during the module can be applied to prediction problems, causal inference, and text analysis. The module will be hands-on, and the theory will be illustrated with empirical applications to economics, finance, and related areas.

Content

• Topics covered may include:
• Advanced overview of linear and logistic regression
• Dimensionality reduction, principal components, and factor models.
• Model selection and shrinkage/regularization with Ridge, LASSO, extensions
• Cross-validation
• Experiments, causal inference, estimation of treatment effects with high-dimensional controls
• Networks
• Classification and clustering
• Latent variable models
• Bagging and the bootstrap
• Decision trees and random forests, neural networks, and deep learning
• Textual analysis.
• Reinforcement learning.

Learning Outcomes

• Subject-specific Knowledge:
• Advanced knowledge of theoretical and practical aspects of key machine learning concepts, principles and methods.

• Subject-specific Skills:
• The ability to apply cutting-edge machine learning techniques to a wide range of potential practical problems.

• Computer literacy and programming skills
• Oral and written communication skills.
• Problem solving and analytical skills
• Planning, organising and time management skills.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module:
• The module is delivered through a combination of lectures and practicals. A combination of lectures, practicals, and guided reading will contribute to achieving the aims and learning outcomes of this module. The summative assignments will test students’ knowledge and critical understanding of the material covered in the module, their analytical and problem-solving skills.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

Self-assessment during practicals. Students will be asked to work on weekly problem sets and submit their work after every practical. During the practicals solutions and extensions will be discussed, as well as guidance for evaluating the work. Generic feedback will be provided after each submission. Each group will present their work during revision week. Students will respond to questions from the module leader and other students. Feedback will be provided during and after the presentation

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University