MSc Artificial Intelligence (Work Experience) MSc

**Please check the Sheffield Hallam University website for the latest information** <br/><br/>**Course summary**<br/>- Study modern AI practices, theories, techniques and ethical considerations.<br/><br/><br/>- Develop your problem-solving skills by applying AI methods.<br/><br/><br/>- Gain in-depth understanding by exploring rigorous development processes.<br/><br/><br/>- Cultivate a substantial portfolio of commercial-quality work.<br/><br/><br/>On this course you’ll learn techniques relevant to the field of AI, from machine learning and to natural language processing. You’ll consider real-world challenges while developing your problem-solving and professional skills – plus a portfolio that enhances your employability in the dynamic field of AI, as well as other related industries.<br/><br/>**How you learn**<br/>This course emphasises hands-on learning in AI and machine learning, covering key areas like neural networks, robotics, natural language processing and computer vision. <br/><br/>You’ll prepare for real-world challenges through practical projects and collaborations with industry partners. The course also focuses on ethical considerations, security, privacy, and AI bias, encouraging a responsible approach to AI. Our expert staff help you gain both theoretical knowledge and practical skills, enhancing your employability.<br/><br/>You learn through: <br/>- Lectures<br/><br/><br/>- Tutorials and seminars<br/><br/><br/>- Regular feedback<br/><br/><br/>- Laboratory sessions<br/><br/><br/>- Real-world problems<br/><br/><br/>- Practical activity-based sessions<br/><br/><br/>- Group and individual project work<br/><br/><br/>- Supervisor support and guidance<br/><br/><br/>- Acting as mentors for new students<br/><br/><br/>- Self-directed study<br/><br/><br/>**Key themes**<br/>You’ll explore technical concepts across key areas of AI, including machine learning foundations, neural networks, cognitive systems for robotic logic and computer vision techniques. When combined, these lead into early course projects such as simulating a self-driving robot and evaluating its success. <br/><br/>You’ll then progress onto larger projects with industry clients that solve real-world problems. During those projects, you’ll have regular scrum meetings with academics and clients that identify narratives, learning needs and action plans to progress. You could find yourself working on a recommendation system for a retail company, or training robot football players to win the robot football world cup. <br/><br/>Ultimately you’ll engage in and with AI research, enhancing your learning and leading into your individual dissertation project. This is your opportunity to tailor your own piece of work in the discipline – as wide ranging as a computer-game-playing robot to screening skin conditions – with regular support from your supervisor.<br/><br/>**Applied learning**<br/>**Work Experience**<br/>A work experience placement is recognised as the ‘gold standard’ opportunity to apply your knowledge within an industry of your choice, to which we expect all our students to aspire. All students are encouraged and supported to undertake a full-time work placement and the course is designed to give you the knowledge, confidence and readiness to secure and succeed in this. To complement this, you have access to various support activities, resources, employer events and opportunities from the Careers & Employability Team to ensure you make the most of your work experience opportunities during your degree. In addition to this support, you will complete a short, seventh module that prepares you for gaining a placement. <br/><br/>In addition, the transformative impact that a placement can have on personal and professional development can be further enhanced through the completion of Sheffield Hallam’s Applied Professional Diploma. The Applied Professional Diploma is a qualification that demonstrates the learning you have gained from the placement, and it is undertaken while on placement not during the normal academic class time.