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Virtual Open Day! 19 November @1PM GMT | Register Now!
Virtual Open Day! 19 November @1PM GMT | Register Now!
Virtual Open Day! 19 November @1PM GMT | Register Now!
Virtual Open Day! 19 November @1PM GMT | Register Now!
10 Credits

Machine Learning

The aim of this course is to introduce students to machine learning, which is a relatively new approach to data analytics at the intersection between statistics, computer science, and artificial intelligence. Students will be taught how to master the theory and the techniques that allow turning information into knowledge and value by “letting the data speak”.

The teaching approach will be based on graphical language and intuition more than on algebra. The course will make use of instructional as well as real-world examples and will balance theory and practical sessions with the software Stata.

​This module can be taken as part of a PG Certificate, PG Diploma or Full Masters Program.

Machine Learning
  • 10 Credits
  • 100 hours of study
  • 15 contact hours
  • 85 hours for private study
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Qualifications accredited by Lancaster University
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Buildable Qualifications
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Learn Around
Your Schedule
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World-Class
Faculty
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Fully Online

Structure

Software

Module Programme

The Basics of Machine Learning I

1 hour
Session Content
  • Supervised vs. unsupervised learning
  • Regression vs. classification problems
  • Inference vs. prediction
  • Sampling vs. specification error

The Basics of Machine Learning II

1 hour
Session Content
  • Parametric vs. non-parametric models
  • The trade-off between prediction accuracy and model interpretability
  • Measuring the quality of fit: in-sample vs. out-of-sample prediction power
  • Goodness-of-fit indices
  • The bias-variance trade-off and the Mean Square Error (MSE) minimization

Simulation, Resampling and Validation Methods I

1 hour
Session Content
  • Logic and functioning of a Monte Carlo experiment
  • Implementing Monte Carlo experiments
  • The logic of the Bootstrap o Bootstrapping standard errors

Simulation, Resampling and Validation Methods II

1 hour
Session Content
  • Cross-Validation
  • The validation set approach
  • Leave-One-Out Cross-Validation
  • K-fold cross-validation
  • The Stata package crossfold

Non-parametric regression: local methods I

1 hour
Session Content
  • Beyond parametric models: the “why” and the “how”

Non-parametric regression: local methods II

1 hour
Session Content
  • Nearest-neighbor regression
  • Kernel-based regression
  • The Stata npregress command

Non-parametric regression: global methods I

1 hour
Session Content
  • Polynomial and series regression
  • Spline regression

Non-parametric regression: global methods II

1 hour
Session Content
  • Generalized additive models

Model Selection and Regularization I

1 hour
Session Content
  • Subset Selection: Optimal, Foreword and Backward subset selection

Model Selection and Regularization II

0.5 hours
Session Content
  • Shrinkage Methods: Lasso, Ridge, and Elastic regression

Tree-Based Regression I

0.5 hours
Session Content
  • An introduction to Regression Trees

Applications using Stata II

0.5 hours
Session Content
  • Bagging, Random Forests, and Boosting

Session Content

Prerequisites

English Language Requirements

Both Programmes are open to applicants anywhere in the world. We may ask applicants to provide a recognised English language qualification, dependent upon their nationality and where they have studied/worked previously.

 The requirement is an IELTS (Academic) Test with an overall score of at least 6.5, and a minimum of 6.0 in each element of the test. We will also consider other English language qualifications. If their score is below our requirements, they may be eligible for one of Lancaster University's pre-sessional English language programmes.

Academic Requirements

Applicants to the Postgraduate Certificate of Achievement, Postgraduate Certificate, Postgraduate Diploma or full MSc in either programme require either an upper second-class degree in economics, econometrics or related subjects.

Learning Outcomes

Key Skills
  • Implement factor-importance detection
  • Perform signal-from-noise extraction
  • Evaluate correct model specification
  • Understand model-free classification, both from a data-mining and a causal perspective
Desired Skills
  • Engage in abstract thinking by extracting the essential features of complex systems to facilitateproblem solving and decision-making
  • Communicate and present complex arguments in oral and written form with clarity andsuccinctness
  • Present, interpret and analyse information in numerical form
  • Utilise effectively statistical and other packages
  • Apply basic statistical techniques to analyse economic and financial datasets
  • Work effectively both individually and within a team environment

Frequently Asked Questions

Are the courses within either programme conducted synchronously or asynchronously?

All sessions are conducted live and online at a scheduled time, but are also recorded. Students may attend live and watch the recordings back to recap the material or watch the recordings only if unable to attend live. We always advise students to attend live where possible as this will allow them the best opportunity to engage with the content and ask the lecturer's questions.

Is all examination undertaken online or in-person?

All modules are examined through online coursework submissions, you will have the support of your module lecturer/tutor in this poccess.

Do I need to buy any statistical/econometric software?

No, all necessary software is provided to students.

What do I do if I can't attend a course live?

All courses are recorded and available on the LUMS internet platform throughout the current academic year. They can therefore be viewed 24 hours a day.

A Collaboration Like No Other

Timberlake Consultants and Lancaster University Management School (LUMS) Economics department have a longstanding partnership; combining 40+ years of industry expertise with over 50 years of academic excellence. We are delighted to build on this with our micro-credential postgraduate courses.

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