Course Catalogue

LJMU Partner Taught

This module will introduce the basic theories of machine learning and artificial intelligence. It will consider the most efficient machine learning algorithms and practical implementation of these algorithms. It will cover the main areas of Artificial Intelligence. Students will gain hands-on experience in getting these techniques to solve real-world problems

Outline Syllabus:Definitions and terminologies of machine learning. Types of learning problems. Supervised learning, unsupervised learning, reinforcement learning, semi-supervised learning, deep learning. Programming languages and tools. Mathematics of machine learning. Machine learning algorithms. Using the programming language or a tool to implement a learning algorithm. Problem definition. Data analysis. Data preparation Implementation of an algorithm. Improving models’ accuracy. Under-fitting situations. Over-fitting situations. Discussion of intelligence and artificial intelligence. Strong AI vs. Weak AI. Top-down approach of AI: Knowledge-based system, natural language processing, fuzzy logic. Bottom up approach of AI: Artificial neural networks, evolutionary computing, swarm intelligence. Applications of AI. Issues of AI. Investigate and demonstrate an AI or ML technique using a programming language or a tool for at least one of the following: knowledge based system, fuzzy logic system, natural language processing. Investigate and demonstrate the technique using the programming language or a tool for at least one of the following: artificial neural network: supervised learning algorithms, single perceptron, MLP & backpropagation learning algorithms. Evolutionary computing: problem model, fitness evaluation, selection method, crossover operator, evolution scheme, observation. Swarm intelligence: swarm intelligent approaches, swarm robotics, team size and composition, team configurability, communication pattern and range.