Displaying 302 resources
Autonomous Underwater Vessels
This lecture overviews Autonomous Underwater Vessels that has many applications in ocean engineering. It covers the following topics in detail: AUV technologies, sensors, communications, applications.
Set Theory
This lecture overviews Set Theory that has many applications in Probability/Statistics, Machine Learning and Computer Vision.
Introduction to Machine Learning
This lecture will cover the basic concepts of Machine Learning to alleviate inconsistencies towards concept and notation accuracy. Supervised, self-supervised, unsupervised, semi-supervised learning. Multi-task Machine Learning.
Article/Books/eBooks
Algorithms with Julia
This book provides an introduction to modern topics in scientific computing and machine learning, using JULIA to illustrate the efficient implementation of algorithms.
Autonomous Surface Vessels
Autonomous marine vessels can be described as surface (boats, ships) and underwater ones (submarines).
Colour Theory
This lecture overviews Colour Theory that has many applications in digital image/video processing and analysis, multimedia sites and in digital TV.
Geometric data analysis based on manifold learning with applications for image understanding
The conference paper gives in the first section a brief and easy understandable introduction into the basics of Riemannian geometry.
Bayesian Learning
This lecture overviews Bayesian Learning that has many applications in pattern recognition and clustering. It covers the following topics in detail: Bayes probability theorem. Bayes decision rule. Bayesian classification.
Image Quality
This lecture overviews Image Quality that has many applications in image filtering, processing, transmission, streaming and TV.
Article/Books/eBooks
Speech and Language Processing
Here's our Jan 7, 2023 draft! This draft is mostly a bug-fixing and restructuring release, there are no new chapters.
Introduction to Statistics
This lecture provides an Introduction to Statistics that has many applications in Data Analytics, Machine Learning and Signal Analysis. It covers the following topics in detail: Random Variables. Data Types. Data Sampling.
Introduction to Autonomous Car Vision
In this lecture, an overview of the autonomous car technologies will be presented (structure, HW/SW, perception), focusing on car vision. Examples of autonomous vehicle will be presented as a special case, including its sensors and algorithms.