Oxford Study Abroad Programme

Online Courses Summer 2022

1 August - 19 August 2022

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.

 

All sessions start with a look at the wider implications of artificial intelligence on society, the economy and the world; and are followed with the technical sessions on the content below, 

*Model building
*Unsupervised learning
*Model fitting
*Beyond linear models: polynomial and logit fits
*Classic ML algorithms
*Gaussian mixture models
*Natural language processing
*Deep learning
*Reinforcement learning

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 Data Science

Course Description
This course introduces the students to the mathematical foundations of data analysis, as well as to visualising and analysing data with Python. There are three main parts to the course. The first third of the course introduces a statistical basis for data analysis, including means and variance of random variables, Bayes’ formula, the central limit theorem, linear regression, confidence intervals and hypothesis testing (z-test and t-test). The second third is about data cleaning and visualisation (including creating plots of various types), with many examples in Python. The final third introduces the basics of machine learning in data analysis, covering the k nearest neighbours algorithm, regression using machine learning and principal component analysis, with an emphasis both on mathematical understanding and the ability to utilise these methods in Python.

 

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 Business and Management - Entrepreneurship and Innovation 

Course Description
This course introduces students to relevant concepts to provide an understanding of business environments and the issues affecting contemporary business. It focuses on the link between entrepreneurial theory and practice. It offers an introduction to some of the key areas of entrepreneurship research, and illustrates these theoretical insights with cases from a wide range of industries, sectors and countries.  

 

Students will learn to reflect on the multifaceted nature of entrepreneurship and strengthen their critical thinking skills through discussion and practical exercises, including the development of a start-up business model. The combination of theory, methodology and practice will help students understand the challenges of entrepreneurship in the real world, achieving a complete view of this phenomenon and its different facets. This course sets the scene and provides a platform for future study as well as helps students to appreciate the interconnected nature of business organisations, the environment in which they operate, and the people involved. It offers undergraduate students and new graduates the opportunity to acquire key skills in management and finance.

 

 

Course 4 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.

 

 

* Please note that the course description is indicative and may be subject to change.

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Scholarships
A limited number of scholarships (normally for university students) are available for those participants who have a competitive performance during the programme and in the home institution.