Research Tutorials in AI and ML
Module aims
This is an interactive module that aims to develop students' ability to critique, summarise and present scientific literature. Focus will be on learning the principles of high-quality academic research in artificial intelligence (AI) and machine learning, including theory, novel algorithmic work, empirical research, as well as literature on AI ethics and regulatory frameworks. Teaching delivery will be carried out in the form of weekly reading group tutorials that are presented by students and critiqued by their peers, under close supervision and steering by the module leads. Additionally, academic writing activities will take place after the sessions with the goal of summarising the paper and key points of the discussion in report form. By the end of the module, students will be able to appraise, evaluate and critically review research work in published form, as well as deepen their understanding of the various AI sub-domains.
Learning outcomes
Upon completion of this module students should be able to:
- Extract and evaluate key information from state-of-the-art work in AI conference proceedings (e.g. NeurIPS, ICLR, ICML, ICCV) and high impact journal publications (e.g. Nature, Science, PNAS), covering theoretical, algorithmic, empirical, and applied research.
- Assess how and why certain research work transformed the understanding of their respective AI sub-domain (e.g. computer vision, NLP, speech, robotics) and why they may have transformative impact on other areas of science (e.g. medicine, agriculture, transport).
- Identify strengths and limitations of publications, by taking into account clarity of hypothesis, datasets used, novelty claimed, algorithmic or empirical contribution, as well as reproducibility of the work.
- Be able to distil complex research concepts into clear, structured summaries; demonstrate ability to lead group discussions and to communicate effectively and concisely with peers across multidisciplinary topics.
- Produce high quality technical reports aimed at audience ranging from domain experts to novice readers. Address individual feedback aimed to improving writing style, clarity, succinctness, and good academic practice.
Module syllabus
Students will attend the sessions for two hours a week over a duration of two terms. They will take turns in presenting a selected scientific paper they have studied on prior the session, and will follow up by writing clear and concise summaries of the work. Papers will be selected carefully by the module leads and will cover a wide range of AI sub-domains, as well as other areas of science where AI may have a direct impact (e.g. medicine, agriculture). The module essentially aims to help students develop three key research skill sets through an interactive format and an individual feedback mechanism. These three skill sets are:
- Critique of high-impact AI and machine learning research papers.
- Delivery of clear and engaging academic presentations to peers.
- Writing high-quality technical reports and summaries in academic English.
Assessments
Journal presentation 50%
Research tutorial summary (report) 50%