SCS-0022
Artificial Intelligence & Machine Learning
Explore advanced artificial intelligence and machine learning concepts, methods and practical applications.
About This Course
This advanced course explores the relationship between artificial intelligence and machine learning and examines how intelligent systems can learn from data, recognise patterns and support decision-making. Students will explore machine learning methods, model development, evaluation, neural networks, responsible AI and practical applications across modern digital environments.
Course Information
What You Will Learn
- Understand advanced concepts and applications of artificial intelligence.
- Explore supervised, unsupervised and other machine learning approaches.
- Prepare, analyse and work with datasets for machine learning projects.
- Understand model training, evaluation and optimisation.
- Explore neural networks and modern AI technologies.
- Apply responsible AI principles, including fairness, transparency and security.
Course Modules
Module 1: Advanced Artificial Intelligence Concepts
This module explores advanced concepts in artificial intelligence and intelligent systems. Students will examine AI approaches, intelligent agents, knowledge representation, reasoning and modern applications of artificial intelligence.
Module 2: Machine Learning Methods and Algorithms
This module explores major machine learning approaches and algorithms. Students will examine supervised and unsupervised learning, classification, regression, clustering and other methods used to identify patterns and make predictions from data.
Module 3: Data Preparation and Feature Engineering
This module focuses on preparing data for machine learning systems. Students will explore data quality, preprocessing, feature selection, feature engineering and methods for preparing suitable datasets for model development.
Module 4: Model Training, Evaluation and Optimisation
This module examines how machine learning models are trained and evaluated. Students will explore training and validation data, performance metrics, model selection, overfitting, optimisation and methods for improving model performance.
Module 5: Neural Networks and Modern AI Technologies
This module introduces neural networks and selected modern AI technologies. Students will explore the principles of neural networks, deep learning concepts, natural language processing, computer vision and other emerging AI applications.
Module 6: Responsible AI and Practical Machine Learning Projects
This module brings together the knowledge developed throughout the course. Students will explore responsible AI, fairness, transparency, privacy, security and practical machine learning projects while considering the social and professional impact of intelligent systems.
Learning Outcomes
- Explain advanced artificial intelligence concepts and intelligent system principles.
- Apply appropriate machine learning approaches to different types of problems.
- Prepare and analyse datasets for machine learning applications.
- Develop an understanding of model training, evaluation and optimisation.
- Explain the fundamental principles of neural networks and modern AI technologies.
- Evaluate machine learning models using appropriate performance measures.
- Apply responsible AI principles when designing and evaluating intelligent systems.
- Develop and communicate practical AI and machine learning project outcomes.
Ready to Start Your Learning Journey?
Register for the SCS-0022 Artificial Intelligence & Machine Learning course and develop advanced knowledge for professional AI and machine learning practice.
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