Program Overview

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University Programs

The university offers a wide range of programs, including short and online courses, undergraduate, postgraduate, professional, and research programs.

Subject Areas

• Archaeology and anthropology
  • Archaeology
  • Theological Studies
• Architectural history
  • Architectural History
• Business and management
  • Business and management
• Data science, computing, maths
  • Data science
  • Computing
  • Maths
• Diplomatic studies and law
  • Diplomatic Studies
• Economics and politics
  • Economics
  • Politics
• Education and study skills
  • Education
• Environment and sustainability
  • Environment
  • Sustainability
• History of art
  • History of Art
• History, including local and social
  • History
• Languages and cultural studies
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• Literature, creative writing and film studies
  • Literature
  • Creative Writing
• Medical and health sciences
  • Medical sciences
  • Health sciences
• Music
• Natural sciences
• Philosophy
• Psychology and counselling
  • Psychology
  • Counselling
• Religion and theology
  • Religion
  • Theology
• Technology and AI
  • Technology
  • AI

Course Format

• Day and weekend events
• In-person learning
• Lecture series
• Online learning
• Professional
• Summer schools
• Weekly learning

Undergraduate Programs

Certificates

• Archaeology
• Certificate of Higher Education
• English Literature
• History
• History of Art
• Theological Studies

Diplomas

• Archaeology
• Creative Writing
• English Social and Local History
• History of Art

Advanced Diplomas

• British Archaeology
• IT Systems Analysis and Design (Online)
• Local History (Online)

Pre-Master's

• Advanced Pre-sessional Course for Graduate Students (nine weeks, full-time)
• Foundations of Diplomacy Pre-Master's Course (six months, full-time)

Summer Schools

• Oxford University Summer School for Adults

Postgraduate Programs

Certificates

• Architectural History
• Cognitive Behavioural Therapy
• Ecological Survey Techniques
• Enhanced Cognitive Behavioural Therapy
• Health Research
• Historical Studies
• Nanotechnology
• Patient Safety and Quality Improvement
• Psychodynamic Counselling
• Qualitative Health Research Methods
• Teaching Evidence-Based Health Care

Diplomas

• Cognitive Behavioural Therapy
• Cognitive Behavioural Therapy Severe Mental Health Problems
• Health Research
• International Wildlife Conservation Practice
• Psychodynamic Practice

Master of Studies (MSt)

• Creative Writing
• Diplomatic Studies
• Historical Studies
• History of Design
• Literature and Arts
• Mindfulness-Based Cognitive Therapy
• Practical Ethics
• Psychodynamic Practice

Research Degrees (DPhil)

• Archaeology
• Architectural History
• Cognitive Behavioural Therapy
• English Local History
• Evidence-Based Health Care
• Literature and Arts
• Sustainable Urban Development

Master of Science (MSc)

• Applied Landscape Archaeology
• Cognitive Behavioural Therapy
• English Local History
• Evidence-Based Health Care
• Evidence-Based Health Care Medical Statistics
• Evidence-Based Health Care Systematic Reviews
• Evidence-Based Health Care Teaching and Education
• Experimental and Translational Therapeutics
• Nanotechnology for Medicine and Health Care
• Surgical Science and Practice
• Sustainable Urban Development
• Translational Health Sciences

Professional Programs

Continuing Professional Development

• Business and management
• Cultural heritage
• Data science, computing, maths
• Diplomatic studies
• Education
• Environment and sustainability
• Medical and health sciences
• Nanotechnology and nanomedicine
• Philosophy and ethics
• Psychology and counselling
• Research methods and skills
• Technology and AI
• Urban studies

Research

Research Community

Research at Oxford Lifelong Learning extends across the disciplines and is supported by a research culture that encourages interdisciplinary initiatives.

Research Areas

• Academic staff profiles
• Part-time DPhil programmes
• Research areas
• Research students

Research Forums

• Artificial Intelligence (AI) Steering Group
• Lifelong Learning Pedagogies forum
• Research Ethics Colloquium
• The Vice-Chancellors Colloquium

About Us

The Department

• Academic staff profiles
• Mission, vision and values
• Our history
• Student spotlights
• Vacancies and tutor panel
• Visiting Fellowships Scheme

News and Events

• News
• Open events
• Whats on

Our Venues

• Accommodation
• Conferences
• Dining and catering
• Facilities

Student Information

• Continuing Education Library
• Oxford qualifications
• Student resources and support

Course Details

Machine Learning and Artificial Intelligence in Python

Overview

Data science is a discipline that uses scientific methods, processes, and algorithms to extract meaningful information, knowledge, and insights from structured and unstructured data.

Programme Details

• Week 1: Introduction to the course. Basic overview of Machine Learning. Linear Regression example.
• Week 2: Overview of a data-science pre-processing pipeline. Exploratory Data Analysis
• Week 3: Data cleaning and preparation.
• Week 4: Supervised Learning: regression.
• Week 5: Supervised Learning: classification.
• Week 6: Decision Trees. Ensemble Methods. Hyperparameter Tuning
• Week 7: Dimensionality reduction and Unsupervised Learning.
• Week 8: The Perceptron. Back-propagation. Fully-connected neural networks.
• Week 9: Deep Learning: fundamental concepts. Transformers and attention.
• Week 10: Deep Learning: other architectures- GANs/Autoencoders

Recommended Reading

• Jupyter Notebook tutorial / Corey Schafer
• 'The 5 Basic Statistics Concepts Data Scientists Need to Know / George Seif
• Python Data Science Handbook / Jake VandeerPlas
• Overview of Machine Learning / Mohit Deshpande

Certification

• Credit Application Transfer Scheme (CATS) points
• Digital credentials

Fees

• Course Fee: ｣360.00

Funding

If you are in receipt of a UK state benefit, you are a full-time student in the UK or a student on a low income, you may be eligible for a reduction of 50% of tuition fees.

Tutor

• Dr Nick Day

Course Aims

• Explore the landscape of contemporary machine learning (ML) and deep learning.
• Learn how to use a variety of machine-learning algorithms to extract features from the data using Python libraries.
• Familiarise with the concepts of overfitting and regularisation in ML.
• Gain insights on how to face scaling issues in a 'big data' scenario.

Teaching Methods

This course takes place over 10 weeks, with a weekly learning schedule and weekly live webinar held on Microsoft Teams.

Learning Outcomes

• Choose the right ML task and evaluation metric for a given ML problem and select a set of ML models to be trained.
• Set up a data pre-processing pipeline for data science and machine learning algorithms.
• Use Python machine learning tools to build up ML models, train and evaluate them on a test set.
• Evaluate whether a model overfits or underfits the data and act accordingly.
• Identify the appropriate and most performant model for a given task and tune appropriately the hyperparameters.

Assessment Methods

You will be set independent formative and summative work for this course.

Application

Experience of using a programming or scripting language is a must. The student should master all the concepts explored in the course Python Programming for Data Science - Introduction prior to enrolling on Intermediate.

Level and Demands

This course is offered at FHEQ Level 4 (i.e. first year undergraduate level), and you will be expected to engage in independent study in preparation for your assignments and for the weekly webinar.

English Language Requirements

We do not insist that applicants hold an English language certification, but warn that they may be at a disadvantage if their language skills are not of a comparable level to those qualifications listed on our website.

Selection Criteria

Before attending this course, prospective students will know all the requirements and topics covered in the "Python Programming for Data Science - Introduction" course.