Teaching
This course has two components – a taught component accounting for two-thirds of your time and effort, and a research component accounting for one-third.
For the taught component, we blend lectures with seminars, syndicate exercises, simulations, and case studies. The majority of modules are taught in small classes to facilitate and encourage interaction. Others practice larger-scale lectures, which are then supported by small class seminars and group activities. Our module leaders have extensive academic and industry experience; guest speakers currently working at the forefront of their fields also contribute regularly to the content and delivery of the course, bringing real-world insight into your learning experience.
In addition to your taught modules, you will undertake a major project as part of your Master's degree. This is nominally 600 hours (60 CATS points) of learning, mainly taking place during the Spring and Summer terms. You will be expected to engage regularly with your Project Supervisor and to provide progress updates and drafts of your work to an agreed schedule.
Class sizes
Overall, this course can accommodate up to 100 students, divided into smaller teaching classes for an enhanced learning experience.
Typical contact hours
Module delivery patterns vary, but most will be delivered in a short learning block of up to 4 weeks, allowing your focus to be on one module at a time. Each module nominally accounts for 150 hours, which includes scheduled classroom time and online sessions as well as your independent study and assessments.
The 30-credit module: ‘Fundamentals of Artificial Intelligence Research, Development, and Management’ spans 7 weeks and includes 10 hours of lectures, 20 hours of seminars, and 15 hours of project supervision. Additionally, students will engage in 135 hours of self-directed study under the guidance of their project supervisor to complete their project plan during this module.
Assessment
The course uses a variety of assessment methods across modules. These may include reports (both topic-based and reflective), individual and group presentations, skills-based assessments in data analysis, programming, implementation of AI and machine learning algorithms to solve challenges, poster presentations, and critical reflections.
Assessments have been designed not only to assess your achievement in meeting the course learning outcomes in an academically sound manner, but also contribute to preparing you with the requisite competencies required for employment.
Reading lists
If you would like to view reading lists for current or previous cohorts of students, most departments have reading lists available through Warwick Library on the Talis Aspire platformLink opens in a new window.
You can search for reading lists by module title, code or convenor. Please see the modules tab of this page or the module catalogueLink opens in a new window.
Please note that some reading lists may have restricted access or be unavailable at certain times of year due to not yet being published. If you cannot access the reading list for a particular module, please check again later or contact the module’s host department.
Your timetable
Core modules will be allocated to students at the end of the first week of term - you will then be able to view your individual module schedule for the rest of the year via the WMG module selection system.
Modules will include scheduled classroom time and online sessions as well as your independent study and assessments, and will usually be delivered within a 4 week timeframe. Occasional classes and study skills sessions may be held at weekends or in the evenings.
As a Master's student, you are expected to manage your own time appropriately. On average, you are expected to commit 38-40 hours of study each week, in order to successfully achieve your Master’s degree.
This is a full-time postgraduate course. Undergraduate term dates do not apply. Whilst there are no holidays as such, there will be no teaching scheduled when the University is officially closed for staff, during the two weeks over Christmas and New Year.
