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Introduction to AI
Hello there! Congrats on making it here, and showing interest in studying Computer Science (CS) at university. Over the next six supervisions together, you will be learning about Artificial Intelligence (AI), an important field of study in CS. As you may already know, AI is everywhere: in personal assistants like Siri or Alexa, in recommender systems like Instagram or Netflix, but also in healthcare, finance, etc. There are few industries which do not use some form of AI nowadays. Even if you decide AI, or even CS is not for you, I believe that it is in your interest to understand it better, as it has become hard to avoid in the world we live in and that is not about to change. Whether you have prior coding experience or not, you will have the opportunity to learn about AI in a hands-on way: I will be introducing you to the Python programming language and the basics of Machine Learning. With these skills, you will go onto building your first AI system. By the end of the course, you should have a complete personal project you can use in both university or internship applications. Hopefully this has gotten you a bit excited. Now, a bit about me. My name is Gabrielle and I am a second-year PhD student in Computer Science supervised by Prof. Paula Buttery, and specialising in Natural Language Processing at the University of Cambridge. I previously completed a masters in Natural Language Processing also in Cambridge, and before that, an undergraduate degree in Computer Science and Mathematics at the University of Edinburgh. Despite having lived my entire life in the UK, I am originally French and attended a French high-school in London, so don’t be surprised if I don’t know much about A-levels. My research interests sit at the intersection of Artificial Intelligence (which will be the main topic of our supervisions together), Linguistics and Education. Specifically, I work closely with Cambridge University Press & Assessment on automating essay assessment and feedback within schools or for large English standardised tests. This is all very specific (as most PhDs are), but if this is of interest to you, feel free to ask me about it. This is my first time designing a course, so I am very open to feedback and suggestions. If there is something specific you would like to ask or learn about, don’t be shy and reach out to me about it! Preparation work: Lab 0. What is AI and why is it important? Homework: Lab 1. What is data and how can we learn from it? Homework: Lab 2. Can AI tell a cat from a dog (classification)? Activity: Lab 3.5. Can AI predict the future (regression)? Homework: Lab 4. What risks does AI pose and how to mitigate them? Homework: personal project. Students each prepared a 5-minute presentation on a personal project they completed during the course. The project could either be an implementation (e.g., an extension on Lab 3 and 3.5, or exploring a new dataset) or a piece of research on an AI topic they were personally interested in or on an idea they were thinking of implementing later. This work is shared under a Creative Commons Attribution-NonCommercial 4.0 International License.Introduction and Overview
Supervision 1: Artificial Intelligence
Supervision 2: Data Science
Supervision 3: Telling a Cat from a Dog
Supervision 4: Predicting the Future with the Past
Supervision 5: Hopes and Fears of AI
Supervision 6: Presentation