CSMAI21-Artificial Intelligence and Machine Learning

Module Provider: School of Mathematical, Physical and Computational Sciences
Number of credits: 20 [10 ECTS credits]
Level:7
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2021/2

Module Convenor: Dr Yevgeniya Kovalchuk
Email: y.kovalchuk@reading.ac.uk

Type of module:

Summary module description:

This module covers the topic of artificial intelligence and machine learning.

Aims:

The aim of the module is to introduce students to current methods in artificial intelligence and machine learning.

Assessable learning outcomes:

Students will be able to:

• Understand the classic and fundamental algorithms of artificial intelligence and the modern machine learning methods, including shallow and deep Artificial Neural Networks.


• Acquire knowledge of artificial intelligence techniques such as problem solving, search, reasoning, planning, learning, and perception.


• Determine appropriate machine learning methods for supervised and unsupervised problems.


• Understand and apply the process of training and making predictions with neural networks.


• Determine the appropriate neural network architecture for a particular problem.


• Apply multiple classes of neural networks to real world problems involving images and text.

Additional outcomes:

Students will gain familiarity with modern machine learning and neural networks libraries with hands-on activities.

Outline content:

The module covers foundational topics in relevant artificial intelligence and machine learning algorithms:

• AI goals and applications areas.


• Problem Solving


• Search and Reasoning


• Probabilistic Classifier.


• Support Vector Machines


• Neural Networks and Deep Learning.


• Backpropagation


• Stochastic gradient descent


• Feedforward and recurrent arch itectures


• Convolutional neural networks


• Generative adversarial networks


• Capsule networks


• Unsupervised LearningÌýÌý


• Reinforcement Learning.


• Applications


• Image Classifications


• Natural Language Processing: Text Classification and Text Generation

Brief description of teaching and learning methods:

The module consists of lectures and weekly guided practical classes that implement methods covered in the lectures.