An introduction to the core concepts and techniques of AI, from search algorithms and knowledge representation to machine learning and the ethical considerations of AI in society. You will gain a foundational understanding of AI's history, evolution, and implications for various industries.

This module bridges the gap between theory and practical applications of machine learning. You will explore core techniques from supervised to unsupervised learning, with emphasis on the entire machine learning pipeline including data pre-processing, model selection, and ethical considerations.

Beginning with foundational concepts, you will progress to cutting-edge deep learning techniques covering perceptrons, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention models. You will gain hands-on experience designing and training networks while addressing explainability and ethics.

This module explores AI-driven decision support systems, their role in diverse industries, and their capability to enhance human decision-making. You will gain insights into AI applications for optimal outcomes, including the role of simulation and the ethical implications of automating decision-making.

The culmination of the course requiring you to demonstrate your learning by identifying critical challenges, conducting rigorous literature reviews, and designing innovative AI solutions. You will conduct independent research and develop an AI solution under the guidance of experienced staff.

This module focuses on distributed data storage, processing frameworks, and the design and deployment of scalable cloud-native applications. You will be trained to tackle challenges associated with managing large volumes of data in the cloud, which is crucial for modern AI solutions.