Low-Code AI Hackathon (online)

What you'll gain from this course

1. Empower yourself by learning AI in a fun and interactive way in a supportive community
2. A chance to learn from experts and gain a University of Oxford certificate
3. Participate in an ongoing community after the hackathon and build networks
4. Address real-life problems 
5. New skills in AI: from basic prompt engineering to creating real solutions, you’ll learn the fundamentals of AI in an easy-to-understand way
6. Career-boosting knowledge: AI skills are in demand across industries. Gain knowledge that will set you apart, opening doors to exciting career opportunities
7. Collaborative creativity: connect with like-minded participants from various fields, share ideas, and create AI-powered solutions that make a difference

Based on a low-code methodology

All you need are prompts… and imagination

This isn’t a traditional hackathon. All you need is prompt engineering and your creativity. Our low-code tools and methodology make it easy for anyone to start building with AI, even if you’ve never written a line of code before. It’s the ideal opportunity to discover how AI can enhance what you already know and do, by solving a problem in a realistic setting.

Approach

For the first time, the combination of LLM (large language models like chatGPT) and low-code development techniques allows non-developers and domain experts to learn AI.

The philosophy of this hackathon is based on how to collaborate with generative AI (an AI co-pilot approach), primarily through prompt engineering.

Participants explore how to create artificial intelligence applications (machine learning and deep learning applications) using low-code and large language models (LLMs), like ChatGPT.  

The hackathon assumes no knowledge of AI, machine learning or coding, but ideally expects that you work within the broader information technology ecosystem.

Upon completing this hackathon, you should be able to understand the process of building machine learning and deep learning applications using generative models and low-code.

The approach covers all the stages of model building (ex data analysis, feature engineering etc.). We use the term ‘low-code’ to mean primarily assisted by large language models.

Problem statement

The problem statement is designed to be on the theme of climate adaptation, including mitigation strategies for the impact of climate change. The topic covers:

1. Risk management (e.g. flooding)
2. Urban habitats
3. Changes in patterns (forests, mangroves etc.)
4. Insurance
5. Health
6. Microfinance
7. Socioeconomic impact
8. Impact on emerging markets and global south  
9. Migration patterns of birds and animals
10. Impact on agriculture and food production
11. Water management
12. Glaciers
13. Engineering

We will formulate a number of small-scale problems low-code. They will cover different geographical areas from the USA to the Global South, as well as a range of themes such as insurance, risks and agriculture. Each participant will need to choose four problems, and to submit an interim design solution two weeks after the start of the course, followed by a final submission two weeks later. All solutions will be open-source.

Who is this course for?

• Individuals with no coding experience but who are curious about AI.
• Domain experts and IT professionals.

Course Delivery

• This is an online asynchronously delivered course

• There will be 6 hours of pre-recorded sessions (there are no live teaching hours).

• Over a four-week period, participants will be provided with online support through a forum for working on their problem statements.
• No attendance at Oxford is required and you do not need to purchase any software.  

Accessing Your Online Course  

Details about accessing the pre-recorded material will be emailed to you during the week prior to the course commencing.