1. Know and be able to articulate the differences between artificial intelligence and machine learning, and to be able to define and give examples of different machine learning techniques.

Knowledge & Understanding, Learning

2. Critically appraise the basic techniques of Machine Learning.

Analysis

3. Discover and use main python libraries to Implement a first linear regression model using a programming language.

Problem solving and Application

A practical assignment which contributes 100% to the module’s marks, consist of developing an Artificial Intelligence prototype artefact (e.g., linear regression model) which will be assessed by demonstration and report. (Learning outcomes 1,2 and 3)

1 : Artificial Intelligence : concepts, applications and principles

- Cognition-based AI and Data-driven AI

- Machine Learning

- Computer vision

- Natural Language Processing

- Conversational AI

- Privacy, Ethics, transparency and accountability



2 : Machine Learning and Data Science

- Supervised versus non supervised

- Regression, Classification and Clustering

- Features and labels in a dataset for machine learning

- Training and test datasets

- Model evaluation metrics

- Feature engineering

- Introduction to Data Science

- Data Science pipeline



3 : Linear Regression in practice

- Applications of linear regression

- Single-variable linear regression

- Multiple Variable linear regression

- Model regularization

- Model performance evaluation (overfitting, bias-variance, corossfolding, ...)

- Data visualization

Artificial intelligence is the set of techniques and theories that render machines capable of simulating human intelligence. We can distinguish two main families: cognition-based and data-driven. While as the first category targets onto reproducing human reasoning and logic, the latter looks for reproducing human behaviour. Data-driven AI comprises all types of machine learning and is largely used nowadays due the huge amounts of ‘Big’ data being produced, stored and processed every single day. In this course students will discover the basic concepts, applications and principles of artificial intelligence. Then the focus will be on machine learning and its applications. Finally, students shall be able to develop a practical use case with python.

The module shall be delivered through lectures and tutorial sessions. Lectures shall provide the theoretical foundation of the area under study whilst tutorials will be used to develop practical skills relating to lectures. The assignment will allow students to develop a deeper awareness of the steps that are required for development of the Machine Learning model.

P. Norvig and S. J. Russell, Artificial Intelligence: A Modern Approach, 3rd Ed., Pearson, 2009

T. Mitchell, Machine Learning, McGraw-Hill, 1997

Blackboard VLE

Python Interpreter and IDE

Access to the library and electronic journals