Flexible learning
Online postgraduate courses
Advanced Machine Learning & Deep Learning Processing
Please note to take this course you must first have completed Foundations of Econometrics, Foundations of Data Science & Programming in Python
This course takes a look at modern machine learning techniques. It takes participants through recent advances in supervised and unsupervised methods and algorithms with a focus on complex business applications. Practical implementations include the use of modern recommendation systems packages, XGBoost, CatBoost, LightGBM, transparent inference, SHAP value analysis, mixture of expert architectures and more. It also provides an introduction to deep learning techniques. More specifically, it reviews the use of deep neural networks, deep reinforcement learning, recurrent nets, and convolutional neural networks among others.
This module can be taken as part of a PG Certificate, PG Diploma or Full Masters Program.
- 20 Credits
- 200 hours of study
- 25 contact hours
- 175 hours for private study
Faculty
Structure
Structure
Advanced Machine Learning is a core 20-credit course and therefore students are expected to input approximately 200 hours of study into the course.
The total number of contact hours is 25 hours. This leaves 175 hours for private study.
Lectures
This module consists of 4- 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.
Module Programme
Introduction to Advanced Machine Learning Techniques
- The role of advanced techniques in solving complex problems
- Overview of popular advanced techniques, including deep learning, reinforcement learning,and generative models
Ensembles
- The basics of model ensembles
- Random forests
- Gradient Boosting, XGBoost, CatBoost
Deep Learning
- What is deep learning and how does it work?
- Successes and limitations of Deep learning approaches
- Implementing deep learning models using popular libraries and frameworks
- Convolutional networks, pooling and dropout
- Natural language processing and Recurrent Neural networks
- Applications of deep learning in computer vision and natural language processing
Reinforcement Learning
- The basics of reinforcement learning and its applications
- Implementing reinforcement learning algorithms using popular libraries and frameworks
- Examples of reinforcement learning in real-world applications
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
- Problem-Solving with ML: Ability to address real-world challenges using machine learning
- Comprehensive ML Knowledge: Broad understanding of diverse machine learning topics
- Deep Learning: Mastery of advanced techniques in deep learning
- Generative Models: Understanding and application of generative models
- Natural Language Processing (NLP): Skills in leveraging NLP for complex problem-solving
- Advanced AI Techniques: Exploration and application of cutting-edge AI techniques
- Reinforcement Learning: Proficiency in applying reinforcement learning methodologies
- Complex Problem Tackling: Application of skills to address intricate real-world problems
- Demonstrate a sound knowledge of supervised and unsupervised learning techniques
- Present, interpret and analyse information in numerical form and use econometric and otherpackages effectively
- Understand the relevance of different econometric approaches to specific applications ineconomics
- Be able to select relevant information from large amounts of data
- Master the advanced tools of machine learning and deep learning
- Communicate and present complex arguments with clarity and succinctness
- Plan and manage time effectively.
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
Subscribe To Our Newsletter
Flexible learning
Online postgraduate courses
Specialist courses
Best-in-field experts
Tailored learning
Career-oriented education