AI and Applied Data Science

Seminars

Participants are taught in small seminar groups of up to 10 students, and receive two one-on-one tutorials with their tutor. 

Sunday

Seminar 1: Introduction to Data Science

Seminar 2: What is AI?

Monday

Seminar 3: Knowledge Engineering

Seminar 4: Starting with Applied Data Science Tools

Tuesday

Seminar 5: Introduction into Applied AI

Seminar 6: Deep Dive into Applied AI tools

Wednesday

Seminar 7: Applied AI – Computer Vision

Seminar 8: Practicing Session on Computer Vision

Thursday

Seminar 9: Natural Language Processing (NLP) and Large Language Models (LLMs)

Seminar 10: Put NLP and LLMs into Practice

Friday

Seminar 11: Applied Data Science and AI Ethics

Seminar 12: Course Summary

Programme timetable

The daily timetable will normally be as follows:

Saturday

14.00–16.30 – Registration

16.30–17.00 – Orientation meeting

17.00–17.30 – Classroom orientation for tutor and students

17.30–18.00 – Drinks reception

18.00–20.00 – Welcome dinner

Sunday – Friday

09.00–10.30 – Seminar

10.30–11.00 – Tea/coffee break

11.00–12.30 – Seminar

12.30–13.30 – Lunch

13.30–18.00 – Afternoons are free for tutorials, individual study, course-related field trips or exploring the many places of interest in and around Oxford.

18.00–19.00 – Dinner (there is a formal gala dinner every Friday to close each week of the programme).

A range of optional social events will be offered throughout the summer school. These are likely to include: a quiz night, visit to historic pubs in Oxford, visit to Christ Church for Evensong and after-dinner talks and discussions.

Residential options are outlined below.

Please see the ‘application’ section above for guidance on how to book or request the right accommodation for you.

Residential option

This includes accommodation and all meals (breakfast, lunch and dinner). 

View full details of Rewley House accommodation.

Non-residential option

We also offer places on a non-residential basis whereby participants can take classes and have meals (lunch and dinner) at Rewley House, having arranged their own accommodation elsewhere.

Course aims

This course aims to:

• Bridge theory with practice, so that learners gain both conceptual understanding and hands-on skills. 
• Introduce the core principles of AI, machine learning, and data science.
• Develop understanding of how algorithms learn from and make predictions with data.
• Cover the data lifecycle: collection, cleaning, processing, analysis, and visualization.
• Facilitate joint activities (mini-projects) and discussions to learn about the impact of Applied AI.

Learning outcomes

By the end of this course, students will have been given the opportunity to understand:

• Data Science Ecosystem
• Computing Cognitive Systems
• Computer Vision
• NLP and LLMS
• AI tools such as as Python, scikit-learn, TensorFlow, and opensource large language models

Teaching methods

The teaching methods used during this course may include:

• Short lectures/presentations
• Physical handouts
• Seminars/group discussions
• Student presentations

Assessment methods

Participants are required to undertake preparatory reading and complete a pre-course assignment of 1,500 words. Although this does not count towards credit, it is seen as an important way of developing your ideas and is mandatory. The pre-course assignment is typically due in the first week of June.

You will be assessed during the summer school by either a 1,000 word written assignment or a presentation supported by individual documentation. To successfully gain credit (10 CATS points) students should attend all classes and complete the on-course assignment.

Participants will attend two one-on-one tutorials with their tutor during the week.

Please see the ‘certification’ section for more details about CATS points.

Dr Sepi Chakaveh – Tutor

Dr Sepideh (Sepi) Chakaveh has a BSc in Electronic Engineering and a PhD in Experimental Astrophysics and Space Sciences from the University of Kent. She has extensive experience as a Senior Scientist/Academic working both in the UK and Germany, including Imperial College London, Max Planck Institute in Heidelberg, University of Göttingen, Fraunhofer Society and the University of Southampton where she was the co-founder and the former Director of Southampton Data Science Academy, the first UK online data science academy. She has recently received the Research Common Room Membership at Wolfson College.

Certificate of Attendance

All participants who complete the course will receive a physical Certificate of Attendance.

Digital badge

You will also be issued with an official digital badge of attendance. After the course, you will receive an email with a link and instructions on how to download this. You will be able to share this on social media and add to your email signature if you wish to do so.

Academic credit

OUSSA is an accredited summer school taught at undergraduate level; each one-week course carries 10 CATS (Credit Accumulation and Transfer Scheme) points at FHEQ (Framework for Higher Education Qualification) Level 4.

CATS points will be awarded to students who attend all classes and complete the on-course assignment to the required standard. Please see the ‘assessment methods’ section below for more details.

Certificate of Higher Education

Credit (CATS points) earned from OUSSA can be transferred towards our flexible Certificate of Higher Education. This part-time, award-bearing course lets you decide what, how and where you study by gaining credit from short courses, including short online courses, in-person weekly classes and OUSSA.

For full details, including transfering credit gained from OUSSA to the programme, see our Certificate of Higher Education programme page.

Fees

Payment

All fees are charged on a per week, per person basis.

Please be aware that all payments made via non-UK credit/debit cards and bank accounts are subject to the exchange rate on the day they are processed.

Course change administration fee: Please note that course transfers may be permitted in exceptional circumstances; however, in accordance with our Terms and Conditions, an administration fee of £50 will be charged.

Payment terms

• If enrolling online: full payment by credit/debit card at the time of booking
• If submitting an application form: full payment online by credit/debit card or via bank transfer within 30 days of invoice date

Cancellations and refunds

Please see the terms and conditions for our open-access courses.

The Department cannot be held responsible for any costs you may incur in relation to travel or accommodation bookings as a result of a course cancellation, or if you are unable to attend the course for any other reason. You are advised to check the terms and conditions carefully and to purchase travel insurance.

Funding

Concessionary rates are available on a non-residential basis for those that qualify. 

The concessionary fee is for non-residential attendance only; participants will then be responsible for finding their own accommodation. See full details including eligibility.

Recommended reading

Christopher Bishop, 2006, Pattern Recognition and Machine Learning. Springer

Aurélien Géron, 2019, Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow. O’Reilly

Joel Grus, 2019, Data Science from Scratch. O’Reilly

Andrew Ng, 2018, Machine Learning Yearning. deepliearning.ai

Module code: O25I603SSR

Most courses fill quickly so early registration is strongly recommended. If your preferred course is fully booked, you may wish to add yourself to the waiting list and the Programme Administrator will contact you should a place become available.

Please note, the programme is only open to those over the age of 18.

Online enrolment (single person accommodation and non-residential)

Single accommodation, double room for 1 person and non-residential places should be booked online by clicking on the ‘Book now’ button at the top of this page.

Online enrolments require payment in full at the time of registering.

Enrolment form (multi-occupancy or accessible accommodation)

Those requiring twin, double or accessible accommodation (including ground/lower floor accommodation) should complete an enrolment form as these rooms cannot be booked or requested online. 

Please send the completed enrolment form to the email address below. Both the PDF and Word option of the form below are editable, so you can complete them online before saving and sending to us as an email attachment. You do not need to print and scan them. (Please use these forms only if you are making a twin or double booking for two people.)

• Enrolment form – editable PDF
• Enrolment form – Microsoft Word

Those who have specific requirements (eg an accessible bedroom) should contact the Programme Administrator directly at oussa@conted.ox.ac.uk or OUSSA, University of Oxford Department for Continuing Education, 1 Wellington Square, OXFORD, OX1 2JA, UK.

View our terms and conditions