The Online Courses are the transition in light of the COVID-19 pandemic. The goal of these changes is to minimise the need to gather in large groups and spend prolonged time in close proximity with each other in spaces such as classrooms, dining halls, and residential buildings. Our actions are consistent with the recommendations of leading health officials on how to limit the spread of COVID-19 and are also consistent with similar decisions made by a number of our peer institutions.
 
Course 1 Artificial Intelligence and Machine Learning

Course Description
This course provides an overview of machine learning techniques to explore, analyse, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems. Our goal is to help you master all the core concepts of both technologies, such as Deep Learning, regression techniques, Machine Learning algorithms, etc. Moreover, during this online course, you will work on a number of real-time, industry-based projects, which will enhance your learning experience and provide you with hands-on experience.

Prerequisites
1.Mathematics 
Students should develop some skills and familiarity with the mathematical topics below. Knowledge of these topics may be acquired by the students before the course (as part of their previous studies).
Whilst this content is not essential for completing the current course, it should be regarded as core learning for students by the time they complete the course.

Mathematical Topics 
● Matrices
   - What a matrix is: Matrix representation of data-sets
   - Matrix operations: Addition (+), Subtraction (-), Multiplication (.), Transpose (T)
   - The link between algebra and matrices: Expressing systems of algebraic equations in matrix form
● Probability
   - What is a ‘probability’?
   - Different views of what a probability represents: Bayesian Vs. Frequentist view
   - Operations on probabilities:’ AND’ and ‘OR’
   - Definitions: ‘Statistical distribution’, ‘Sample Space’, ‘Random Variable’
   - Discreet Vs. Continuous Random Variables and the relationship between them
   - Expectation: Definition and use in valuing options
 

2.The course programming language - Python

Python Programming Language
   - You do not need to be highly skilled at Python before starting the course.
   - The majority of activities will require you to read and replicate existing code, but not to write new programmes.
We will be using the ‘Jupyter Notebook’ environment to write Python programmes.
   - You will need access to your own Jupyter Notebook environment in order to complete the hands-on and practical workshop element of this course.
   - Jupyter is free and widely available. But you will need to either install it on your own machine or otherwise access a public Jupyter environment.

 Course 2 Robotics and Artificial Intelligence

Course Description
This course provides an overview of the field of robotics. Topics related to how robots move, perceive and interact will be discussed. The first half of the course will cover fundamental concepts in robot kinematics, computer vision, robot control, localization and planning. In the second half, the course will cover the foundations of machine learning and AI and explain how a neural network is built and trained. Each topic is accompanied by a programming tutorial where the students will learn to implement the concepts in a hands-on way.

Prerequisites
1.Programming:
Familiarity with the basics of Python
Python 3.7 or 3.8 installed as a part of the Anaconda Python distribution of Data Science, or equivalent.

2.Mathematics and statistics: 
Working knowledge of linear algebra, calculus, basic probability, and statistics.
 

Course 3 Future Cities and Public Policy

Course Description
As the world becomes increasingly urbanized, societies face new and complex challenges arising from intense economic pressure, increased inequality, and environmental degradation. Beyond a traditional role of guiding land use and development projects, contemporary urban planners are responsible for promoting more competitive, inclusive, and ecological cities.

In this course, you will study cities through the lens of economic, social, and environmental sustainability. The course’s world-class faculty delivers state-of-the-art courses, personalized mentoring, and firsthand insight on projects they have conducted in the public, private, and non-profit sectors. By working with them and your peers, you’ll learn how to leverage knowledge of cities into forward-looking policy and action for advancing the goals of societies across the globe. Issues such as poverty, inequality, production and consumption in the urban communities of the future are key in the development of sustainable cities.

Tuition Fee Reductions
1. Partner University Student Discount of GBP 100
Each applicant from the partner universities is offered a favourable discount of £100 off the original tuition fee of £1, 250.
2. Additional Tuition Fee Reduction of GBP 100
An Additional Tuition Fee Reduction of GBP 100 is eligible for the students with
1)    a minimum cumulative weighted GPA of 3.5/4.0 or equivalent,e.g.,
a weighted average mark of 85/100 (China Grading System), 
or an upper second-class (2:1) /average percentage score of 60% (UK Grading system), 
or an academic class rank in the top 30%, 
or equivalent at the home university
2)    And an A+, A or A- (about 30%-35% of all participants) on the final assessment of the course