8443 modules
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COMP6252 2025-26
Deep Learning Technologies
Deep learning has revolutionised numerous fields in recent years. We've witnessed improvements in everything from computer vision through speech analysis to natural language processing as a result of the advent of massively parallel compute coupled with large datasets. This module explores how deep learning can be applied to real world data by implementing models through combinations of pre-built building blocks. -
COMP6252 2026-27
Deep Learning Technologies
Deep learning has revolutionised numerous fields in recent years. We've witnessed improvements in everything from computer vision through speech analysis to natural language processing as a result of the advent of massively parallel compute coupled with large datasets. This module explores how deep learning can be applied to real world data by implementing models through combinations of pre-built building blocks. -
COMP6252 2028-29
Deep Learning Technologies
Deep learning has revolutionised numerous fields in recent years. We've witnessed improvements in everything from computer vision through speech analysis to natural language processing as a result of the advent of massively parallel compute coupled with large datasets. This module explores how deep learning can be applied to real world data by implementing models through combinations of pre-built building blocks. -
COMP6252 2029-30
Deep Learning Technologies
Deep learning has revolutionised numerous fields in recent years. We've witnessed improvements in everything from computer vision through speech analysis to natural language processing as a result of the advent of massively parallel compute coupled with large datasets. This module explores how deep learning can be applied to real world data by implementing models through combinations of pre-built building blocks. -
AICE3005 2028-29
Deep Reinforcement Learning for Robotics
Unlocking the potential of AI requires autonomous systems that can independently learn to make good decisions. Deep reinforcement learning is a powerful framework for this, with particular relevance to robotics, enabling robots to autonomously acquire and refine complex skills through interactions with the world without manually programming the desired behaviour. This module will cover the principles and current advances of the field of deep reinforcement learning and its application to robotic skill learning. It begins by introducing the theoretical foundations of reinforcement learning and the role of deep neural networks as function approximators. The module then explores important methods and state-of-the-art approaches focused on learning to control robotic agents for game playing or performing physical tasks. This culminates in practical applications of deep reinforcement learning for key robotic control problems. -
SOES6008 2027-28
Deep Sea Ecology
This module examines the patterns of life in deep-sea environments & the processes that govern those patterns. -
SOES3055 2028-29
Deep Sea Ecology
This module examines the patterns of life in deep-sea environments & the processes that govern those patterns. -
SOES6008 2025-26
Deep Sea Ecology
This module examines the patterns of life in deep-sea environments & the processes that govern those patterns. -
SOES6008 2026-27
Deep Sea Ecology
This module examines the patterns of life in deep-sea environments & the processes that govern those patterns. -
SOES3055 2027-28
Deep Sea Ecology
This module examines the patterns of life in deep-sea environments & the processes that govern those patterns.