Abstract
The students will learn how machines can engage in learning, reasoning and planning with a special focus on Machine Learning (ML). The subject will discuss recent developments in the areas of Natural Language Processing (NLP) and Computer Vision (CV). The subject will also empower students with development skills in AI … For more content click the Read More button below.
Syllabus
Mathematical FoundationsIntroduction to Artificial Intelligence, History and ApplicationsAgent Search Strategies and ReasoningPython for AI ApplicationsBiological Nets and PerceptronThe Learning Rules and Gradient DescentMultilayer Perceptron and BackpropagationDeep Learning: the BasicsRecognition with Computer VisionUnderstanding Natural Language ProcessingRobotics and Reinforcement Learning
Learning outcomes
Upon successful completion of this subject, students should:
1.
be able to understand the fundamental theories, evolution, and applications of AI;
2.
be able to evaluate various AI search algorithms, such as uninformed, informed, heuristic, and constraint satisfaction;
3.
be able to understand the basic requirements for designing neural networks and machine learning models;
4.
be able to apply machine learning solutions to real-world problems; and
5.
be able to evaluate and use existing models for solving natural language processing and/or computer vision problems.
Enrolment restrictions
Available to undergraduate students only.
Pre-requisite
Pre-requisite