- Imperial College London via Coursera
- Learn for FREE, Up-gradable
- 24 months of effort required.
- 12 Modules Included.
- £28,000 Total cost.
- Completely Online.
- Language: English
Join a booming, in-demand field with a Master’s degree in Machine Learning and Data Science from one of the top 10 universities in the world. In this programme delivered by the Department of Mathematics at Imperial College London, you will develop an in-depth understanding of machine learning methods, alongside invaluable practical skills and guided experience in applying them to real-world problems. The curriculum is designed to propel your engineering or data science career forward, allowing you to choose the path that’s right for you, be that a role as a data scientist, a machine learning engineer, or a computational statistician.
With hands-on projects, you’ll build a portfolio to showcase your new skills in everything from probabilistic modeling, deep learning, unstructured data processing and anomaly detection.
You will not only build a strong foundation in Mathematics and Statistics, giving you confidence in your analytical skills, but you will also acquire expertise in implementing scalable machine learning solutions using industry-standard tools such as PySpark, ensuring that no data is too big or too complex for you. You will also have the opportunity to broaden your horizons through one of the first of its kind study of ethical issues posed by machine learning. You will graduate with an ability to go beyond the algorithms and turn data into actionable insights, contribute to strategic decision making in your organisation and become a responsible member of this rapidly growing profession.
Imperial, ranked #10 in the world by Times Higher Education (2020 World University Ranking), is home to numerous eminent world-class researchers in machine learning, many of which will be contributing to this programme. It has had a rich history in driving innovation since the beginning of this field: John Nelder, Professor at Imperial College, helped developed GenSim, the precursor to R and the first proper implementation of a general framework for regression. The university maintains close ties with industry and a number of pioneering tech companies, some of which will be contributing to the programme by way of project ideas for your MSc thesis.
What makes this Machine Learning degree unique?
Imperial is home to world-famous mathematicians, including three winners of the Fields Medal, which recognizes outstanding mathematics achievement.
With one of the strongest and most awarded mathematics departments in the UK, Imperial produces deep thinkers capable of pioneering new research into today’s most pressing scientific and technological problems.
Unlike other master’s in data science programmes that teach Machine Learning with a computer science focus, this degree prepares students with the mathematical and statistical theory needed to truly understand machine learning, as well as the practical skills to deal with real world applications that they need to be successful in their careers.
The programme will train students in the mathematical, computational, and statistical foundations of machine learning, giving them the ability to critique data analysis and implement scalable machine learning solutions.
Students will also have the opportunity to broaden their horizons by participating in a programme-spanning module, the first of its kind, in ethics of machine learning and AI transparency, covering techniques to offset potential limitations and biases introduced by machine learning.
Coursework will enable students to develop an in-depth understanding of the theories behind machine learning methods, alongside invaluable practical skills in Python and R to solve real world problems.
Cohorts and Deadlines
Due to the circumstances of COVID-19, it was decided to delay the launch of the programme until October 2021.
Applications are expected to reopen this Fall 2020.
Requirements for Admission
- At least a 2.1 UK Bachelor’s Degree in Statistics, Mathematics, Engineering or Physics. The academic requirement of a minimum 2.1 is for applicants who hold or who are working towards a UK qualification. For guidance on how qualifications awarded by non-UK institutions may satisfy the College’s minimum academic admission requirements – see Imperial’s Country index.
- All Imperial applicants must also show that they have a high level of written and spoken English to meet the demands of our challenging academic environment (IELTS 7.0). Find out more about our English language requirements for postgraduate study.
- Create an application via the Imperial College ‘Apply’ webpage – this is where you will submit your application.
- Prepare the information you will need to complete your application, including:
1) personal statement,
2) academic transcript(s),
3) CV/resume, and
4) referee contact details.
We aim to review applications in detail once all information has been submitted. The process takes 6-8 weeks.
All documentation-including references and proof of English language proficiency-are mandatory. We are unable to make a decision on your application if we have not received your references or other supporting information related to qualifications or english language requirements, so it is important this information and documents are submitted in a timely manner via the Imperial College online system.
The MLDS degree is a fully online degree part-time programme, delivered and structured over two-years, with three terms per academic year.
Year one modules:
- Ethics in Data Science and Artificial Intelligence (Part 1)
- Programming for Data Science
- Applicable Maths
- Exploratory Data Analytics and Visualisation
- Supervised Learning
- Big Data: Statistical scalability with PySpark
- Ethics in Data Science and Artificial Intelligence (Part 2)
- Bayesian Methods
Year two modules:
- Deep Learning
- Unsupervised Learning
- Ethics in Data Science and Artificial Intelligence (Part 3)
- Unstructured Data Analysis
- Learning Agents
- Research Project
With hands-on projects, students build a portfolio using industry-standard tools such as PySpark to showcase their new skills in applications such as probabilistic modeling, deep learning, unstructured data processing and anomaly detection. Relationships with corporate partners provide students the opportunity to address issues from different sectors, such as finance, software, health and medicine, engineering, and government.
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