Programmes

Machine Learning in Practice

  • Summer schools
  • Department of Statistics
  • Application code SS-ME315
  • Starting 2021
  • Short course: Closed
  • Location: Houghton Street, London

From chatbots, personalised recommendations on social media, traffic predictions and virtual personal assistants including Siri and Alexa, advances in machine learning are becoming an integral tool that help individuals navigate the modern world.

Increased adoption of such technology across the world has driven massive growth in the volume of data, requiring businesses to harness the power of machine learning to make decisions, learn about and predict customer behaviour to drive strategic advantage.

Combining the fields of engineering, statistics, mathematics and computing, machine learning is one of the leading data science methodologies revolutionising business. This course will cover a wide range of machine learning methods, both model-based and algorithmic. Presenting the theoretical foundations of these methodologies, you will have an opportunity to apply them using real-world examples and datasets.

Computer seminars which enable you to practice your programming skills and a course project give you the opportunity to explore how machine learning can be used innovatively to solve pressing business challenges such as algorithmic trading in the financial industry, predicting customer behaviour, and improving compliance and risk management. By the end of the course, you will have developed the ability to understand how machine learning can be integrated into current business models and the challenges that this poses.

 


Session: Two
Dates: 12 – 30 July 2021 


 

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

Assessments*: An individual project (50%) and a two-hour written exam (50%)

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


*Assessment is optional but may be required for credit by your home institution. You will need to check with your home institution that this course meets their credit requirements.

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

Prerequisites

You should have completed at least one semester of calculus, and at least one semester of probability and statistics to undertake this course. Some minimal experience with computer programming is also required.

Key topics

  • Introduction to machine learning and its application
  • Supervised and unsupervised learning
  • Statistical inference and probability theory
  • Linear regression and related methods
  • Classification techniques, logistic regression and discriminant analysis
  • Tree-based methods
  • Graphical models and Markov graphs
  • Neural networks and deep learning
  • Gaussian processes and support vector machines

Programme structure and assessment

This course is delivered as a combination of lectures, computer seminars, class discussions, problem sets and readings. The computer seminars will give you an opportunity to develop your programming skills and apply the theory using datasets.

The course is assessed through an individual project (50%) and a final examination (50%). Problem sets and computer-based exercises will be provided in class for feedback, allowing you to check your understanding of the course content and to prepare you for the examination. These exercises will not contribute to your final grade.

*Further details will be provided at the beginning of the course.

Course outcomes

  • Show in-depth knowledge of supervised and unsupervised machine learning algorithms
  • Learn to perform some of the main techniques and algorithms for regression, classification, tree-based methods and graphical models in R
  • Discuss the application of clustering and principal component analysis, neural networks and deep learning at an introductory level
  • Develop an understanding of the process to learn from data and inform decisions on real-world problems
  • Apply and evaluate suitable methods to various datasets by model selection and predictive performance assessment

Is this course right for you?

This course is designed for students from various disciplines that use data to inform decision making. It is suitable if you want an in-depth understanding of how machine learning can be integrated into modern business. You should also consider taking this course if you are targeting a career in IT, data science, marketing, research, consulting and business management. It is equally suited if you are a professional already working in industry, government, or research organisations, looking to develop your understanding of this rapidly developing field.

Your department

LSE’s Department of Statistics has earned an international reputation for the development of statistical methodology that has grown from its long history and active contributions to research and teaching in statistics for the social sciences.

Students have the opportunity to engage with some of the most rapidly developing topics transforming business and society today, including machine learning, big data forecasting, social media, and text and network analysis. As a result, the department is meeting the rising demand for professionals with the skills to work with new datasets and who can conduct meaningful research. Students can develop these sought-after data science skills which will prepare them for careers in a wide range of sectors including the financial, government, non-profit and public sectors.

Your faculty

Lecturers:
Dr Kostas Kalogeropoulos
Associate Professor
Department of Statistics

Dr James Abdey
Assistant Professorial Lecturer
Department of Statistics

Reading materials

The main reading material will consist of lecture slides and related materials, which will be distributed at the beginning of the course. Optional further reading is also recommended from the following textbooks

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

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

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

*A more detailed reading list will be supplied prior to the start of the programme
**Course content, faculty and dates may be subject to change without prior notice

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How to Apply

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