Artificial Intelligence Concepts: Applied Statistics (Online)


artificial intelligence, n.

The capacity of computers or other machines to exhibit or simulate intelligent behaviour; the field of study concerned with this.

source: Oxford English Dictionary

Artificial Intelligence (AI) has become ingrained in the fabric of our society, often in seamless and pervasive ways that may escape our attention day-to-day. The ability of machines to sense, process information, make decisions and learn from experience is a transformative tool for organisations, from governments to big business. However, these technologies pose challenges, including social and ethical dilemmas.

This course introduces concepts and techniques in Artificial Intelligence that are grounded in Applied Statistics, including the fields of probability theory and big data analysis. Classical and Bayesian approaches are introduced to estimate parameters, quantify uncertainty, and test models in an Artificial Intelligence context. Techniques explored include Bayesian networks, regression, self-organising maps, decision trees and ensemble methods. The course considers the potential and pitfalls of Artificial Intelligence applications based on big data analysis. It is aimed at a general audience, including professionals whose work brings them into contact with AI and those with no more than a passing acquaintance with AI.

This is part of a series of courses that aim to confer an appreciation of how AI has already transformed our world, explain the fundamental concepts and workings of AI, and equip us with a better understanding of how AI will shape our society so that we can converse fluently in the language of the future.

This course makes extensive use of mathematical notation consistent with its level as a first-year undergraduate course (FHEQ level 4). The course does not involve any coding and instead focuses on concepts in Artificial Intelligence for a general audience.

Programme details

Unit 1: Introduction to Probability Theory

  • Randomness versus vagueness
  • Frequentist and Bayesian interpretations of probability
  • Sample spaces and the set-theoretical model of probability
  • The axioms of probability
  • Conditional probability


Unit 2: Bayesian Learning

  • Biased coins: limitations of the frequentist approach
  • Advantages of the Bayesian approach for Machine Learning
  • Bayesian estimation: maximising the posterior distribution
  • Optimal Bayes, Gibbs and Naive Bayes classifiers


Unit 3: Bayesian Networks

  • Independent probabilities, pairwise independence and mutual independence
  • Conditional independence
  • Graph theory and notation
  • Constructing Bayesian networks
  • Bayesian networks for prediction


Unit 4: Introduction to Statistics

  • Random variables, expected values and standard deviations
  • Sample means and the update formula
  • Computing point estimates
  • Confidence intervals, Chebyshev’s theorem and the empirical rule
  • Performing hypothesis tests
  • Visualising data: histograms, box plots and scatter plots


Unit 5: Self-Organising Maps

  • The concept of competitive learning
  • Self-organising maps and their applications
  • Training self-organising maps
  • Variations on self-organising maps
  • Visual representations of self-organising maps


Unit 6: Information Theory

  • Binary and multi-class classification problems
  • Binary decision rules for binary classification problem
  • Entropy and mutual information
  • Decision trees for binary classification problems
  • Constructing efficient decision trees via mutual information


Unit 7: Ensemble Methods

  • Applications and suitability of ensemble methods
  • Bootstrapping
  • Boosting to improve model accuracy
  • Naive Bayes and ensemble methods


Unit 8: Regression

  • Linear models with dependent and independent variables
  • Regression versus classification
  • Calculating regression coefficients in a linear regression


Unit 9: Working with Data

  • Big data analytics: challenges and opportunities
  • Describing the properties of datasets
  • Data analysis pipelines
  • Big data and big corporations


Unit 10: Risks and Benefits of Big Data

  • Exploiting big data analysis for prediction, adaptability and value delivery
  • Concrete examples of successful implementations
  • The legal and technical challenges of big data applications
  • Internal and external security concerns regarding big data
  • Biases in big data analytics



To earn credit (CATS points) for your course you will need to register and pay an additional £10 fee for each course you enrol on. You can do this by ticking the relevant box at the bottom of the enrolment form or when enrolling online. If you do not register when you enrol, you have up until the course start date to register and pay the £10 fee.

See more information on CATS point

Coursework is an integral part of all online courses and everyone enrolled will be expected to do coursework, but only those who have registered for credit will be awarded CATS points for completing work at the required standard. If you are enrolled on the Certificate of Higher Education you need to indicate this on the enrolment form but there is no additional registration fee.

Assignments are not graded but are marked either pass or fail.

All students who successfully complete this course, whether registered for credit or not, are eligible for a Certificate of Completion. Completion consists of submitting the final course assignment. Certificates will be available, online, for those who qualify after the course finishes.


Description Costs
Course Fee £350.00
Take this course for CATS points £10.00


Mr Peter Kiss

Peter Kiss is pursuing a PhD in theoretical computer science at the University of Warwick. He has completed his undergraduate and master’s degree at Worcester College, University of Oxford in mathematics and computer science. Peter teaches courses in algorithm design and discrete mathematics as a senior graduate teaching assistant at Warwick. He has contributed to online courses taught by the continuing education department. Over his undergraduate years he has mentored several successful prospective undergraduate students throughout their Oxbridge admissions.   

His research mainly concerns dynamic algorithm design. He is further involved in research regarding streaming and distributed algorithms. He has published in medical journals as a machine learning research scientist. 

Course aims

  • To introduce fundamental concepts in Artificial Intelligence from Probability and Statistics
  • To study a wide range of Artificial Intelligence approaches arising from Applied Statistics
  • To identify the possibilities and pitfalls presented by Big Data and Artificial Intelligence

Learning outcomes

By the end of this course, students should:

  • Understand fundamental concepts in Artificial Intelligence from Probability and Statistics
  • Understand how Artificial Intelligence draws on statistical foundations
  • Have detailed knowledge of specific Applied Statistics methods
  • Be able to identify suitable Applied Statistics approaches for Artificial Intelligence applications
  • Be able to critically assess the potential impact and pitfalls of Big Data for Artificial Intelligence.

Assessment methods

You will be set two pieces of work for the course. The first of 500 words is due halfway through your course. This does not count towards your final outcome but preparing for it, and the feedback you are given, will help you prepare for your assessed piece of work of 1,500 words due at the end of the course. The assessed work is marked pass or fail.

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. If you are confident in your proficiency, please feel free to enrol. For more information regarding English language requirements please follow this link:


Please use the 'Book' or 'Apply' button on this page. Alternatively, please complete an application form.

Level and demands

FHEQ level 4, 10 weeks, approx 10 hours per week, therefore a total of about 100 study hours.

IT requirements

This course is delivered online; to participate you must to be familiar with using a computer for purposes such as sending email and searching the Internet. You will also need regular access to the Internet and a computer meeting our recommended minimum computer specification.