Modern Concepts of Dynamical Neural Networks

2025/2026

Academic Year

ENG

Instruction in English

6

ECTS credits

Delivered at:

School of Data Analysis and Artificial Intelligence

Course type:

Compulsory course

When:

1 year, 3, 4 module

Instructor

Srinivasan, Sabarathinam

Course Syllabus

Abstract

This course delves into the modern concepts of Dynamic Neural Networks (DNNs), representing the forefront of artificial intelligence and deep learning innovation. Dynamic Neural Networks stand out with their ability to adapt their architecture and functionality dynamically, responding to varying inputs and operational conditions. Students will explore state-of-the-art techniques such as dynamic routing, layer andneuron adaptation, and advanced attention mechanisms, including transformers. The course also covers cutting-edge topics like meta-learning, few-shot learning, and neural architecture search (NAS), which enable DNNs to optimize their structure and learning processes in real-time. Additionally, the course will address the implementation ofresource-aware and sparse networks, crucial for deploying AI on evices with limited computational capabilities. Through a blend of theoretical knowledge and hands-on projects, participants will gain insights into how these modern DNN concepts are revolutionizing applications in computer vision, natural language processing, robotics, and ersonalized recommendations. This course is ideal for AI researchers, practitioners,and enthusiasts eager to master the latest advancements in dynamic neural network technology.