Are you interested in identifying unusual or unexpected patterns in a dataset? Then you may want to learn about Anomaly Detection! This binary classification technique aims to flag data that deviates significantly from the majority within a dataset. By doing so, potential errors, fraud, or other types of unusual events can be rooted out and investigated further.
What is Anomaly Detection?
Anomaly Detection, also known as Outlier Detection, is a way of identifying data that is significantly di
Autonomous driving is a topic gaining a lot of attention in recent years. It refers to the ability of vehicles to drive themselves without the need for human intervention. This technology has the potential to revolutionize the way we travel, making transportation safer, more efficient, and more accessible to all.
How does autonomous driving work?
Autonomous vehicles use a combination of sensors, communications technology, and AI algorithms to navigate roads and highways safely. These sensors
Facial recognition and modelling have become increasingly popular in recent years thanks to advancements in technology and machine learning. Facial recognition is the ability of a computer or machine to identify or verify a person's identity based on their facial features, while facial modelling involves creating a digital representation of a person's face for various purposes.
What is Facial Recognition?
Facial recognition technology uses a combination of machine learning algorithms and arti
Fraud Detection is essential in various industries such as finance, banking, government agencies, insurance, and law enforcement, among others. With the rise of fraudulent activities in recent years, it has become crucial to have effective fraud detection mechanisms in place. Despite the efforts of organizations, they still lose millions of dollars every year to fraud. Detecting fraud in significant datasets can be challenging, as only a small fraction of the population is involved in fraudulent
Overview of Intrusion Detection
Intrusion Detection is an important method of monitoring a computer system or network to detect unauthorized access or security breaches. It involves dynamically analyzing various events occurring in a network or system to identify potential security problems. The system works by automatically collecting information from different sources and analyzing them to detect any suspicious activity or sign of intrusion.
Intrusion Detection has become an essential elemen
What is Knowledge Tracing?
Knowledge Tracing is a process that helps to understand how much a student knows about a topic or subject. The goal of Knowledge Tracing is to create a model that can predict how well a student will perform on future assignments or interactions. By doing so, it can suggest resources based on individual needs so that students can learn more efficiently.
How Does Knowledge Tracing Work?
Knowledge Tracing uses data collected from students' interactions with educationa
Learning-to-Rank: Using Machine Learning to Build Ranking Models
If you've ever searched for something on Google or scrolled through a news feed on social media, you've benefited from learning-to-rank. Learning-to-rank is the application of machine learning to build ranking models. Ranking models are used to sort information in order of relevance or importance. Therefore, they are essential in information retrieval and news feeds applications.
What are Ranking Models?
Ranking models are soph
What is Malware Classification?
Malware Classification is the process of identifying and assigning a malware sample to a specific malware family. Malware is any type of software that is malicious and intended to harm a computer system, network or device. Various types of malware include viruses, worms, trojans, ransomware, adware, spyware and more. A malware family consists of a group of malwares that share similar properties, which can be used to create signatures for their detection and class
Open set learning (OSL) is a new approach to the traditional concept of supervised learning. It is a more realistic and challenging way to train classifiers to detect test samples that fall outside of the training data. This means that the labels of the test samples may be from classes that were not seen during training.
The Open Set Recognition Sub-task
The sub-task of open set recognition (OSR) involves the detection of test samples that do not belong to the training set. In other words, OS
Question Answering is a type of machine learning task that involves answering questions based on a given context. The task is typically performed on reading comprehension questions, where an AI system is trained to read a passage of text and answer questions related to that passage.
Types of Question Answering
Question answering can be segmented into various types, including domain-specific tasks like community question answering and knowledge-base question answering. In a community question
Robotic grasping is the task of using robotic arms to pick up and hold objects of various shapes, sizes, and weights. This task involves using deep learning techniques to identify the best way to grasp objects in different scenarios. The process includes analyzing dynamic environments and identifying unknown objects to ensure that the robotic arm can grasp them efficiently.
The Importance of Robotic Grasping
Robotic grasping is essential in various industries, including manufacturing, logisti
What is Transfer Learning?
Transfer learning is a machine learning technique where an already trained model is utilized to solve a different but related problem. The concept of transfer learning is to leverage the knowledge gained from a previously trained algorithm to help another algorithm solve a related problem efficiently, quickly, and accurately. Transfer learning is a valuable tool for machine learning because it allows developers, researchers, and designers to train accurate models for
Unsupervised Anomaly Detection: Understanding the Basics
In today's technological landscape, large amounts of data are generated every second. This data is generally characterized into normal and abnormal data. Normal data is what is considered as the standard or regular data, while abnormal data are events or objects that are rare or outside the norm. Detecting anomalies in large data sets is very important because they can cause harm, lower the accuracy of models, and lead to data breaches. T