Displaying 295 resources
Introduction to Autonomous Systems
A fully autonomous system can: a) gain information about the environment, b) work for an extended period without human intervention, c) move either all or part of itself throughout its operating environment without human assistance and d) avoid situa
1D Convolutional Neural Networks
This lecture overviews 1D Convolutional Neural Networks that has many applications in 1D signal analysis.
Road Condition Assessment
This lecture overviews Road Condition Assessment that has many applications in autonomous car perception and smart city management. It covers the following topics in detail:
A Survey of Large Language Models
This survey highlights the complexity of language and the challenge of developing AI algorithms capable of understanding and generating language.
Stereo and Multiview Imaging
Stereoscopic and multiview imaging will be explored in depth, as they have tremendous applications in many applications, ranging from autonomous car/drone/robot/vessel vision to Surveying Engineering to Medical Imaging.
Computational Cinematography
This lecture overviews Computational Cinematography that has many applications in filming, notably in drone cinematography.
Human Action Recognition
This lecture overviews Human Action Recognition (HAR) that has many applications in semantic video content description, indexing, retrieval, video surveillance and Human – Computer Interaction (HCI).
2D Systems
This lecture overviews 2D Systems, as they are the primary tools for many image processing and analysis operations. It covers the following topics in detail: Two-Dimensional Discrete LTI Systems. 2D convolutions. 2D correlation.
Understanding and mitigating bias in AI automated systems
“The AI community has been focusing on developing fixes for harmful bias and discrimination, through so-called ‘debiasing algorithms’ that either try to fix data for known or expected biases, or constrain the outcomes of a given predictive model to p
Digital Image Filtering
This lecture overviews Image Filtering, which is very important to ensure high image quality and image denoising. It covers the following topics in detail: Image noise. 2D FIR filters, including Moving average filters.
Lecture notes on reinforcement learning
Reinforcement learning is an appealing subject. Firstly, it is a very general concept: an agent interacts with an environment with the goal to maximize
the rewards it receives from the environment.
Recommendation Systems
This lecture overviews Recommendation Systems that has many applications in Web Science, Marketing and Social Media Analytics. It covers the following topics in detail: Content Based Filtering.