Flexible learning
Online postgraduate courses
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.
- 10 Credits
- 100 hours of study
- 15 contact hours
- 85 hours for private study
Faculty
Structure
Structure
Machine Learning is an elective 10 credit course and therefore students are expected to input approximately 100 hours of study into the course.
The total number of contact hours is 15 hours. This leaves 85 hours for private study.
Lectures
This module consists of 2 - hour lectures per day for 5 days, plus a 1 - hour tutorial per day.
There will be optional clinics on the last day of the course.
The dates of each lecture are confirmed closer to the start of each term. If you have any questions about dates, please contact edu@timberlake.co.uk.
Stata
Module Programme
The Basics of Machine Learning I
- Supervised vs. unsupervised learning
- Regression vs. classification problems
- Inference vs. prediction
- Sampling vs. specification error
The Basics of Machine Learning II
- 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
- 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
- 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
- Beyond parametric models: the “why” and the “how”
Non-parametric regression: local methods II
- Nearest-neighbor regression
- Kernel-based regression
- The Stata npregress command
Non-parametric regression: global methods I
- Polynomial and series regression
- Spline regression
Non-parametric regression: global methods II
- Generalized additive models
Model Selection and Regularization I
- Subset Selection: Optimal, Foreword and Backward subset selection
Model Selection and Regularization II
- Shrinkage Methods: Lasso, Ridge, and Elastic regression
Tree-Based Regression I
- An introduction to Regression Trees
Applications using Stata II
- Bagging, Random Forests, and Boosting
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
- 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
- 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
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.
All modules are examined through online coursework submissions, you will have the support of your module lecturer/tutor in this poccess.
No, all necessary software is provided to students.
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.
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.
Flexible learning
Online postgraduate courses
Specialist courses
Best-in-field experts
Tailored learning
Career-oriented education
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Flexible learning
Online postgraduate courses
Specialist courses
Best-in-field experts
Tailored learning
Career-oriented education