Nanyang Technological University Study Camp

Topic: Robotics, Automation and Internet of Things

14 – 21 December 2025

Theories and Applications in Artificial Intelligence

Introduction

This course will introduce the fundamental theories and applications of Artificial Intelligence (AI). AI is widely considered as one of the most important technologies in recent years. Its impacts have been significant in many fields including business, manufacturing, education, healthcare, transportation, etc. This course will cover the recent developments in AI and introduce some important AI models and architectures including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), emerging trends such as Transformers, Diffusion Models and their relevant applications. At the end of the course, participants will have a good understanding of these important AI models, theories and their applications.

 

Course Objectives

This course aims at providing you with a good understanding of the fundamental theories and applications of AI. You will learn different models of AI including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), emerging trends such as Transformers, Diffusion Models, and their relevant applications. If you are interested in the theories and applications of
AI, you should take this course. This course will equip you with the key knowledge of AI for your future advancement in the relevant fields.

 

Course outline

Session 1: Introduction to Artificial Intelligence (AI) History of AI, Deep Neural Network (DNN) architectures, linear classifier, AI resources

Session 2: Convolutional Neural Networks (CNNs) Theory of CNNs, CNN architecture, training of CNNs, well-known CNN models, applications such
as image classification.

Session 3: Recurrent Neural Networks (RNNs) Theory of RNNs, RNN architecture, training of RNNs, theory of LSTMs, LSTM architecture,
applications such as time-series prediction.

Session 4: Emerging Trends in AI Emerging trends in AI such as Transformer, Generative Adversarial Network, Diffusion Models,
etc.

Session 5: Group Presentation by Participants Teamwork and group presentation by participants

 

Duration

3-hour x 5 sessions = 15 hours

Teaching Methodology

In class teaching or online teaching via Zoom / Teams

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About the Trainer