Programmes

ME318: Theoretical Foundations of Data Science and Machine Learning

  • Summer schools
  • Department of Statistics
  • Application code SS-ME318
  • Starting 2020
  • Short course: Closed
  • Location: Houghton Street, London

UPDATE: Due to the global COVID-19 pandemic we will no longer be offering this course in summer 2020. Please check our latest news on this situation here.

The aim of the course is to develop basic quantitative tools that are necessary for a deep understanding of data science and machine learning. In particular, the course is for participants who are curious ‘why’ and ‘how’ some important machine learning algorithms work.

Topics covered in the course include: bias-complexity trade-off and the no-free lunch theorem; curse of dimensionality; linear prediction; stochastic gradient descent; support vector machines; decision trees; nearest neighbors; neural networks and backpropagation. The course also explains how some of the algorithms can be implemented in Python.

The course is largely self-contained and reviews the necessary mathematical concepts. In particular, it reviews relevant concepts in linear algebra and covers necessary probabilistic ideas. A quantitative background is necessary. Some programming experience would be desirable.


Session: Two
Dates: 13 July – 31 July 2020
Lecturers: Professor Johannes Ruf and Professor Mihail Zervos


 

Programme details

Key facts

Level: 300 level. Read more information on levels in our FAQs

Fees:  Please see Fees and payments

Lectures: 36 hours 

Classes: 18 hours

Assessment*:  Two written examinations.

Typical credit**: 3-4 credits (US) 7.5 ECTS points (EU)


*Assessment is optional

**You will need to check with your home institution

For more information on exams and credit, read Teaching and assessment

Prerequisites

Calculus and linear algebra at lower undergraduate level as well as an introductory course in probability or statistics. Some programming experience would be desirable but is not essential.

Programme structure

Specific topics include:

  • Review of mathematical concepts
  • Bias-complexity trade-off and the no-free lunch theorem
  • Curse of dimensionality
  • Linear prediction
  • Stochastic gradient descent
  • Support vector machines
  • Decision trees
  • Nearest neighbors
  • Neural networks and backpropagation
  • Implementations in Python

Course outcomes

After successfully completing the course, students will understand the theoretical foundations of data science and machine learning. In particular, they will learn how important machine learning techniques, such as nearest neighbors and decision trees, work. Students will gain experience in implementing these techniques.

Teaching

The LSE Department of Mathematics is internationally recognised for its teaching and research. Located within a world-class social science institution, the department aims to be a leading centre for Mathematics in the Social Sciences. The Department of Mathematics was submitted jointly to REF 2014 with LSE's Department of Statistics: 84% of the research outputs of the two departments were classed as either world-leading or internationally excellent in terms of originality, significance and rigour.

The Department has more than doubled in size over the past few years, and this growth trajectory reflects the increasing impact that mathematical theory and mathematical techniques are having on subjects such as economics and finance, and on many other areas of the Social Sciences.

On this three week intensive programme, you will engage with and learn from full-time lecturers from the LSE’s mathematics faculty.

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How to Apply

Related Programmes

Statistical Methods for Multivariate Data in Social Science Research

Code(s) SS-ME303

Introduction to Calculus

Code(s) SS-ME100

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