Graduate Diploma in Machine Learning and Artificial Intelligence (University of London)

• An acceptable Bachelor's degree;
• An acceptable Master's degree (or any other appropriately accredited Level 7 award) provided this is at least 1 year full time in duration.
• Plus:
• GCSE Mathematics (Grade A - B) or equivalent

Equivalent International Qualifications

For information on international qualifications, refer to SIM’s International Student Prospectus.

Other Requirements

English Language Requirements

• Demonstrate a good level of English to be admitted to the programmes.

UOL accepts a range of evidence, including proficiency test scores. Those without evidence may be considered on a case-by-case basis by UOL.

The explosive and ever-growing use of technology in business and commerce means that there’s a whole range of different career possibilities for computing graduates. In terms of job opportunities and salaries, the IT sector is well ahead of most other industrial and commercial sectors.

This programme was designed for students who would like to have a strong foundation in computer science and specialist knowledge in different areas including: artificial intelligence, user experience (UX), virtual reality and web development, depending on the graduate diploma completed. Typical job titles include:

• Machine Learning Engineer
• Software Engineer: Machine Learning
• Systems Analyst

Structure

• This programme will run over two semesters a year. Student can study up to four new modules in one semester; or two plus final project.
• All class are conducted on SIM campus unless otherwise stated.
• A blended learning approach is adopted. Besides learning via online resources, SIM prepare students for discussion, coursework or project work; face-to-face sessions emphasize on discussions, case studies, and hands-on exercises. In class, the lecturer facilitates discussion and learning. As such, a large amount of time is spent on a mixture of:
• • lectures
  • lab works
  • computer simulation sessions
  • online learning through Coursera platform*
• Local faculty support from SIM and online academic support from Goldsmiths, University of London.
• Average teacher-student ratio: 1:56
• Academic materials include
• • Coursera
  • Virtual Learning Environment (VLE)
  • SIMConnect portal
  • University materials such as subject guides, past exam papers and exam commentaries, reading lists and handbooks on good study strategies
• Classes are held in three-hour blocks between Monday and Friday, starting at 8.30am, 12pm, or 3.30pm. There are occasional classes on weeknights at 7pm and weekends.
• Minimum class size to commence is 25 students. Students will be informed within 30 days after the application period.

* Coursera: The BSc Computer Science (CS) programme is fully developed and taught by the same faculty that teaches on-campus at University of London. The University of London leverages Coursera’s online education platform to deliver the programme curriculum, allowing our students to benefit from Coursera features such as interactive video transcription, in-course note taking, and seamless learning across multiple devices.

At SIM, lecturers guide students to leverage the resources available on Coursera and facilitate the learning that takes place. The supplementary readings, video lectures, assignments, and discussion forums are extensively discussed in class. Students also collaborate on group projects using Zoom and Slack. Students may access all course materials anywhere with the mobile app on Coursera, available on iOS and Android.

Using the mobile app, learners can: (1) Save a week’s worth of content for offline access with one click (2) Save and submit quizzes offline (3) View text transcripts of lecture videos (4) Take notes directly in the app (5) Set reminder alerts to help you make progress.

Assessment & Attendance

• Modules: Each module, excluding the Final Project, is assessed either by coursework or a combination of coursework and a two-hour unseen written examination.
  • Note: Each coursework element may consist of multiple items of assessment. The pass mark for any element of assessment is 40%.
• Final Project: The summative assessment for the Final Project consists of both coursework and a written examination, weighted in the ratio 80:20. The written examination consisting of questions relating to your project.
• Each item of coursework, totalling 80% of the overall mark for the Final Project is weighted as follows:
• • Project proposal - Pass / Fail
  • Progress logs - 5%
  • Preliminary project report - 10%
  • Project presentation video - 5%
  • Final project report and code - 60%
• Attendance requirement
  • Local: 75%
  • International (inclusive of Dependent Pass/Long-term Visit Pass holders): 90%

Modules

Each module is assessed either by coursework or a combination of coursework and a two-hour unseen written examination.

Students are will be expected to enroll and complete the following in the course of their study:

• Three core modules
• Three compulsory modules
• One Final Project

Core Modules

• CM2015 Programming with data
• CM3015 Machine learning and neural networks
• CM3020 Artificial intelligence

Compulsory Modules

• CM3010 Databases and advanced data techniques
• CM3060 Natural language processing
• CM3065 Intelligent signal processing

Final Project

PLUS a compulsory project:

• CM3070 Final project