Unsupervised learning - PowerPoint PPT Presentation


Descriptive Data Mining

Descriptive data mining analyzes historical data to find patterns, relationships, and anomalies, aiding in decision-making. Unsupervised learning and examples of techniques like clustering are explored, showcasing the power of data analysis in business.

1 views • 49 slides


Artificial Intelligence and Computer-Related Inventions

Explore the key concepts and techniques in the field of artificial intelligence (AI), including supervised learning, unsupervised learning, reinforcement learning, deep learning, and generative adversarial networks. Gain insights into the evolving definitions of intelligence in machines and the pote

4 views • 13 slides



Comprehensive Overview of Autoencoders and Their Applications

Autoencoders (AEs) are neural networks trained using unsupervised learning to copy input to output, learning an embedding. This article discusses various types of autoencoders, topics in autoencoders, applications such as dimensionality reduction and image compression, and related concepts like embe

4 views • 86 slides


Understanding Large Language Models in Generative AI

Large Language Models (LLMs) like chatGPT are statistical pattern-recognition systems that predict the next word in a sequence based on the context. Trained on vast datasets, LLMs cluster words by understanding patterns, not true meaning. They use unsupervised learning and reinforcement to improve r

10 views • 29 slides


Netdata - The Open Source Observability Platform: A Comprehensive Overview

Netdata is an open-source observability platform created by Costa Tsaousis. It enables real-time, high-resolution monitoring with auto-discovery of integrations, unsupervised machine learning for metrics, alerting, visualization, and anomaly detection. With easy installation on any system, Netdata p

1 views • 54 slides


Graph Neural Networks

Graph Neural Networks (GNNs) are a versatile form of neural networks that encompass various network architectures like NNs, CNNs, and RNNs, as well as unsupervised learning models such as RBM and DBNs. They find applications in diverse fields such as object detection, machine translation, and drug d

2 views • 48 slides


Hands-on Machine Learning with Python: Implement Neural Network Solutions

Explore machine learning concepts from Python basics to advanced neural network implementations using Scikit-learn and PyTorch. This comprehensive guide provides step-by-step explanations, code examples, and practical insights for beginners in the field. Covering topics such as data visualization, N

2 views • 13 slides


Deep Reinforcement Learning Overview and Applications

Delve into the world of deep reinforcement learning on the road to advanced AI systems like Skynet. Explore topics ranging from Markov Decision Processes to solving MDPs, value functions, and tabular solutions. Discover the paradigm of supervised, unsupervised, and reinforcement learning in various

0 views • 24 slides


Multi-Heuristic Machine Intelligence for Automatic Test Pattern Generation

The 31st Microelectronics Design and Test Symposium featured a virtual event discussing the implementation of multi-heuristic machine intelligence for automatic test pattern generation. The presentation covered motivation, modus operandi, experimental results, conclusions, and future works in the fi

1 views • 17 slides


Understanding Multidimensional Scaling and Unsupervised Learning Methods

Multidimensional scaling (MDS) aims to represent similarity or dissimilarity measurements between objects as distances in a lower-dimensional space. Principal Coordinates Analysis (PCoA) and other unsupervised learning methods like PCA are used to preserve distances between observations in multivari

2 views • 21 slides


Difference Between Supervised and Unsupervised Learning

If you want to learn more about supervised and unsupervised learning, you should enroll in a financial modeling training course online.

0 views • 10 slides


Machine Learning Algorithms and Models Overview

This class summary covers topics such as supervised learning, unsupervised learning, classification, clustering, regression, k-NN models, linear regression, Naive Bayes, logistic regression, and SVM formulations. The content provides insights into key concepts, algorithms, cost functions, learning a

0 views • 39 slides


Understanding Autoencoders: Applications and Properties

Autoencoders play a crucial role in supervised and unsupervised learning, with applications ranging from image classification to denoising and watermark removal. They compress input data into a latent space and reconstruct it to produce valuable embeddings. Autoencoders are data-specific, lossy, and

0 views • 18 slides


Understanding Sentiment Classification Methods

Sentiment classification can be done through supervised or unsupervised methods. Unsupervised methods utilize lexical resources and heuristics, while supervised methods rely on labeled examples for training. VADER is a popular tool for sentiment analysis using curated lexicons and rules. The classif

7 views • 17 slides


Overview of Unsupervised Learning in Machine Learning

This presentation covers various topics in unsupervised learning, including clustering, expectation maximization, Gaussian mixture models, dimensionality reduction, anomaly detection, and recommender systems. It also delves into advanced supervised learning techniques, ensemble methods, structured p

1 views • 37 slides


Seminar on Machine Learning with IoT Explained

Explore the intersection of Machine Learning and Internet of Things (IoT) in this informative seminar. Discover the principles, advantages, and applications of Machine Learning algorithms in the context of IoT technology. Learn about the evolution of Machine Learning, the concept of Internet of Thin

0 views • 21 slides


Innovative Learning Management System - LAMS at Belgrade Metropolitan University

Belgrade Metropolitan University (BMU) utilizes the Learning Activity Management System (LAMS) to enhance the learning process by integrating learning objects with various activities. This system allows for complex learning processes, mixing learning objects with LAMS activities effectively. The pro

4 views • 16 slides


Classification of Lidar Measurements Using Machine Learning Methods

This study focuses on classifying lidar measurements using supervised and unsupervised machine learning methods. By utilizing machine learning, specifically supervised learning, the researchers trained a prediction function to automatically label unlabeled lidar scans. They conducted steps to implem

0 views • 16 slides


Unsupervised Learning: Complexity and Challenges

Explore the complexities and challenges of unsupervised learning, diving into approaches like clustering and model fitting. Discover meta-algorithms like PCA, k-means, and EM, and delve into mixture models, independent component analysis, and more. Uncover the excitement of machine learning for the

0 views • 71 slides


Convolutional Neural Networks for Sentence Classification: A Deep Learning Approach

Deep learning models, originally designed for computer vision, have shown remarkable success in various Natural Language Processing (NLP) tasks. This paper presents a simple Convolutional Neural Network (CNN) architecture for sentence classification, utilizing word vectors from an unsupervised neura

0 views • 15 slides


Exploring Self-Supervised Audio-Visual Learning for Segmentation Tasks

Researchers from the Weizmann Institute of Science delve into the realm of self-supervised audio-visual learning for segmentation tasks, leveraging the correlation between visual and audio events to jointly train networks for enhanced understanding. Motivated by the potential of unsupervised learnin

0 views • 44 slides


Introduction to Advanced Topics in Data Analysis and Machine Learning

This content delves into advanced topics in data analysis and machine learning, covering supervised and unsupervised learning, classification, logistic regression, modeling class probabilities, and prediction using logistic functions. It discusses foundational concepts, training data, classification

0 views • 28 slides


Exploring the Potential of Big Data Analytics in Transaction Banking

Big Data Analytics (BDA) offers valuable insights in transaction banking through varied data sources and methods like Supervised, Unsupervised, and Reinforcement learning. Use cases include anomaly detection, fraud detection, default prediction, forecasting, and sentiment analysis. Discussions also

0 views • 8 slides


Understanding Conceptualization in Machine Learning

Discussion on two types of representations (Propositional, Non-propositional) and the role of similarity in categorizing stimuli. Exploring supervised and unsupervised categorization methods, along with the capabilities of conceptualization beyond classification and clustering. Comparison of human a

0 views • 21 slides


Unsupervised Speech Disentanglement with SpeechSplit 2

The SpeechSplit 2 method addresses the challenge of modifying specific aspects of speech while keeping others unchanged. By leveraging techniques like VAE-based approaches, GAN-based methods, and contrastive learning, SpeechSplit 2 disentangles speech into rhythm, content, pitch, and timbre componen

0 views • 12 slides


Machine Learning and Generative Models in Particle Physics Experiments

Explore the utilization of machine learning algorithms and generative models for accurate simulation in particle physics experiments. Understand the concepts of supervised, unsupervised, and semi-supervised learning, along with generative models like Variational Autoencoder and Gaussian Mixtures. Le

0 views • 15 slides


Understanding Machine Learning: Types and Approaches

Machine learning involves various types of learning strategies, ranging from skill refinement to knowledge acquisition. This spectrum includes caching, chunking, refinement, and knowledge acquisition. Differentiating between supervised and unsupervised learning, understanding how machines learn is p

0 views • 45 slides


Introduction to Machine Learning in BMTRY790 Course

The BMTRY790 course on Machine Learning covers a wide range of topics including supervised, unsupervised, and reinforcement learning. The course includes homework assignments, exams, and a real-world project to apply learned methods in developing prediction models. Machine learning involves making c

0 views • 62 slides


Understanding Neural Network Learning and Perceptrons

Explore the world of neural network learning, including topics like support vector machines, unsupervised learning, and the use of feed-forward perceptrons. Dive into the concepts of gradient descent and how it helps in minimizing errors in neural networks. Visualize the process through graphical ex

0 views • 54 slides


Understanding Online Learning in Machine Learning

Explore the world of online learning in machine learning through topics like supervised learning, unsupervised learning, and more. Dive into concepts such as active learning, reinforcement learning, and the challenges of changing data distributions over time.

0 views • 49 slides


Evolution of Machine Learning and Deep Learning in AI

Exploring the evolution of machine learning and deep learning in artificial intelligence through neural networks, with insights on supervised, unsupervised, and reinforcement learning. Learn about recommended resources like Java Weka and Python scikit-learn for data mining tasks. Delve into advancem

0 views • 16 slides


Machine Learning in Geosciences and its Applications

Explore the intersection of machine learning and geosciences, covering topics like paleontology, gravity, structural stratigraphy, geochemistry, sedimentology, convolutional neural networks, seismology, planetology, exploration, kernel methods, ensemble learning, and more. Delve into the three major

0 views • 6 slides


Unsupervised Multiword Expression Extraction Using Measure Clustering Approach

Goal of this study is to develop an unsupervised method for extracting multiword expressions (MWEs) like idioms, terms, and proper names of different semantic types. The research focuses on properties of MWEs, data analysis, statistical measures, and clustering results to supplement lexical resource

0 views • 44 slides


Understanding Unsupervised Learning: Word Embedding

Word embedding plays a crucial role in unsupervised learning, allowing machines to learn the meaning of words from vast document collections without human supervision. By analyzing word co-occurrences, context exploitation, and prediction-based training, neural networks can model language effectivel

0 views • 25 slides


Unsupervised Learning Paradigms and Challenges in Theory

Explore the realm of unsupervised learning as discussed in the Maryland Theory Day 2014 event. Overcoming intractability for unsupervised learning, the distinction between supervised and unsupervised learning, main paradigms, NP-hardness obstacles, and examples like the inverse moment problem are co

0 views • 38 slides


Unsupervised Relation Detection Using Knowledge Graphs and Query Click Logs

This study presents an approach for unsupervised relation detection by aligning query patterns extracted from knowledge graphs and query click logs. The process involves automatic alignment of query patterns to determine relations in a knowledge graph, aiding in tasks like spoken language understand

0 views • 29 slides


Understanding Machine Learning: Types and Examples

Machine learning, as defined by Tom M. Mitchell, involves computers learning and improving from experience with respect to specific tasks and performance measures. There are various types of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. Supervise

0 views • 40 slides


Understanding Latent Variable Models in Machine Learning

Latent variable models play a crucial role in machine learning, especially in unsupervised learning tasks like clustering, dimensionality reduction, and probability density estimation. These models involve hidden variables that encode latent properties of observations, allowing for a deeper insight

0 views • 10 slides


Understanding Artificial Intelligence: A General Overview

Artificial Intelligence (AI) is a fancy term for statistical modeling, with two main types: unsupervised learning to spot patterns and supervised learning to predict the future. AI democratization is changing the landscape by making AI more accessible, leading to both new opportunities and challenge

0 views • 8 slides


Unsupervised Machine Translation Research Overview

Delve into the world of unsupervised machine translation research focusing on the challenges of low-resource languages, lack of parallel corpora hindering system development, and the solutions and efficient approaches adapted by researchers. Explore the agenda covering semi-supervised and unsupervis

0 views • 28 slides