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Live Masterclass 
Engage with Imperial academics live online

Course details

  • Duration: 7 x 2 hours live lectures delivering via Microsoft Teams with group discussions and questions.              Web based exercises and quizzes will be provided for formative feedback. Group projects for assessing the learning outcomes, supported by 7 x 2 hour tutorials.1 x 2 hour session on final day for project presentation.

      Total learning hours: 30 hours 

Everyone knows data is essential, but society still needs to gain the skills and tools to understand large datasets. Humans and AI applications are producing more data than ever, so it becomes more important to process the data and draw the right conclusion by understanding the limitations of your models.

This master class will give participants with no experience of AI, machine learning and programming,  an understanding of these technologies and apply the knowledge and learning experience to design and develop machine learning techniques specific to real-world datasets. The course also focuses on Python as a programming language, one of the most popular options for numeric computations and machine learning.

Topics covered include:

  • Introduction to the Python programming language
  • Numerical processing

This will be a hands-on guide on dealing with data for a typical machine learning pipeline in Python, plus all the fundamental skills required to process data effectively.

To introduce the main numerical processing libraries in Python, and how high dimensional computations can be performed effectively.

  • Machine Learning
  • Model output interpretation and results interpretation

To teach the basics of machine learning and the difference between various models, losses, and optimisation algorithms.

To understand the limitations of the trained models, look at the different performance metrics and be able to debug errors.

Live classes will be delivered on weekdays between 08:00 and 12:00 UK time / 16:00 to 20:00 China time.

Project work will be done through team-based learning and tutorials. Final projects will be presented in groups on the last day of the programme. A prize will be awarded to the team with the best project.

The programme will be delivered over Microsoft Teams. Online project channels will be allocated to each team for project work. Students will be able to use the channel at any time to work on their project.

The entire programme will be taught in English.

More information

Key learning objectives

On completion of this masterclass, you’ll be able to: 

  • Process data in Python and produce a basic Machine Learning pipeline.
  • Understand the basic knowledge about various data types, storage, encoding and decoding techniques.
  • Apply the knowledge and experience gained to develop a Machine Learning project and understand the performance metrics.

Entry requirements

This masterclass is designed for undergraduate or postgraduate students studying in Engineering, Computing, Software Engineering, Math, Physics from a well-recognised university in China.  Students are NOT expected to have AI, machine learning or programming experience.

English requirements:

All students are required to have a good command of English, and if it is not their first language, they will need to satisfy the College requirement as follows:

  • a minimum score of IELTS (Academic Test) 6.5 overall (with no less than 6.0 in any element) or equivalent.
  • TOEFL (iBT) 92 overall (minimum 20 in all elements)
  • CET- 4 (China) minimum score of 550
  • CET- 6 (China) minimum score of 520

Technical requirements:

Students are not expected to have programming experience but willing to learn Python skills for data manipulation and machine learning.

Students will need to have access to a computer with a webcam, microphone and good internet connection to attend the live classes.

Certification

Students will receive a verified Imperial College London digital certificate on successful completion of this masterclass and a prize will be awarded to the best project team. Each student will also receive a transcript for their project marks.