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.
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This module can be taken as part of a PG Certificate, PG Diploma or Full Masters Program.
Key Skills
By the end of this course, participants should have knowledge and ability to:
Problem-Solving with ML: Ability to address real-world challenges using machine learning.
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Natural Language Processing (NLP): Skills in leveraging NLP for complex problem-solving.
Comprehensive ML Knowledge: Broad understanding of diverse machine learning topics.
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Advanced AI Techniques: Exploration and application of cutting-edge AI techniques.
Deep Learning: Mastery of advanced techniques in deep learning.
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Reinforcement Learning: Proficiency in applying reinforcement learning methodologies.
Generative Models: Understanding and application of generative models.
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Complex Problem Tackling: Application of skills to address intricate real-world problems.
Desired Skills
By the end of this course, students should be able to:
Demonstrate a sound knowledge of supervised and unsupervised learning techniques.
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Present, interpret and analyse information in numerical form and use econometric and other packages effectively.
Understand the relevance of different econometric approaches to specific applications in economics.
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Be able to select relevant information from large amounts of data.
Master the advanced tools of machine learning and deep learning.
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Communicate and present complex arguments with clarity and succinctness.
Plan and manage time effectively.
Structure
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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.
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Lectures
This module consists of 4- hour lectures per day for 5 days, plus a 1 - hour tutorial per day.
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During their private study, students should read the course literature, work on practical exercises, and write a project report for the examination.
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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.