Machine Learning in Practice

  • Summer schools
  • Department of Statistics
  • Application code SS-ME315
  • 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.

Machine learning is one of the leading data science methodologies building prediction and decision frameworks using data. It is being adopted extensively due to its ability to solve problems in the presence of large datasets. Examples include commercial tasks such as search engines, recommender systems (e.g., Netflix, Amazon) and advertising. Moreover, the use of machine methods is increasing rapidly in financial institutions for algorithmic trading, predicting customer behavior, compliance and risk.

Machine learning combines the fields of engineering, statistics, mathematics and computing. This course will cover a wide range of machine learning methods, both model-based and algorithmic. It illustrates the applications of these methods through real-world examples and datasets. In most cases, it also presents the theoretical foundation of these methodologies.

The course is suitable for students coming from any discipline that involves the use of data to inform decisions on real world problems. At the same time, professionals from industry, government, or research organisations are also welcome. Students should be familiar with the basic concepts of statistics (up to linear regression) and have some basic understanding of calculus and linear algebra. Some minimal experience with computer programming is also required.


Session: One
Dates: 22 June – 10 July 2020
Lecturer: Dr Kostas Kalogeropoulos


Programme details

Key facts

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

Fees:  Please see Fees and payments

Lecturers: 36 hours

Classes: 18 hours



An individual project that will be assigned on Tuesday of week two and will be due at the end of the course. It will be worth 50% of the overall grade.

Final exam:

A two-hour written exam at the end of the course. The exam will be worth 50% of the overall grade.

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


At least one semester of calculus, and at least one semester of probability and statistics. Some minimal experience with computer programming is also required.

Programme structure

Topics to be covered:


  • An overview of machine learning and its applications, definitions of supervised and unsupervised learning as well as an introduction to some basic concepts such as parametric vs. non-parametric models, prediction accuracy vs. model interpretability and over-fitting.
  • Essential concepts of Statistical inference and probability theory. Model inference and assessment.
  • Linear regression and related methods including variable selection and regularisation.
  • Classification techniques: logistic regression and discriminant analysis.
  • Unsupervised Learning: Principal Component and Factor Analysis, Clustering and mixture models.
  • Tree-Based Methods: Regression and Classification Trees, Boosting and Random Forests.
  • Graphical Models: Markov Graphs, Estimation with known and unknown structure.
  • Introduction to Neural Networks and Deep Learning.
  • Further non-linear methods such as Gaussian Processes and Support Vector Machines.

Course outcomes

Course Aims and Objectives:

  • To provide an in-depth knowledge of supervised and unsupervised machine learning algorithms.
  • To perform some of the main techniques and algorithms for regression, classification, tree-based methods and graphical models in R.
  • To learn other topics in unsupervised learning (clustering and principal component analysis), neural networks and deep learning at an introductory level.

 Course outcomes: At the end of the course and having completed the essential reading and activities students should be able to:

  • Develop an understanding of the process to learn from data and inform decisions on real-world problems.
  • Gain familiarity with a wide variety of algorithmic and model-based machine learning methods and be able to apply them in R.
  • Apply and evaluate suitable methods to various datasets by model selection and predictive performance assessment.


Department of Statistics at LSE has a distinguished history. Its roots can be traced back to the appointment of Sir Arthur Lyon Bowley, an alumnus of the University of Cambridge, at LSE in 1895. He was appointed Chair in Statistics in 1919, probably the first appointment of its kind in Britain. The Department of Statistics was submitted jointly to REF 2014 with LSE's Department of Mathematics: 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 an international reputation for development of statistical methodology that has grown from its long history of active contributions to research and teaching in statistics for the social sciences.

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

Reading materials

The main reading material will consist of lecture slides and related materials, that will be distributed at the beginning of the course, as well as the following textbook:

- Hastie, T., Tibshirani, R. and Friedman, J. The Elements of Statistical Learning: Data Mining, Inference and Prediction. 2nd Edition, Springer, 2009.

Optionally, the following books might also be helpful:

- K. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.

- D. Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press 2012.

- C. M. Bishop, Pattern Recognition and Machine Learning, Springer 2006.

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How to Apply

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