Overview of the state-of-the-art Time Series Clustering based on literature study; distance metrics, prototypes, time-series preprocessing, and clustering algorithms
The document discusses Long Short Term Memory (LSTM) networks, which are a type of recurrent neural network capable of learning long-term dependencies. It explains that unlike standard RNNs, LSTMs use forget, input, and output gates to control the flow of information into and out of the cell state, allowing them to better capture long-range temporal dependencies in sequential data like text, audio, and time-series data. The document provides details on how LSTM gates work and how LSTMs can be used for applications involving sequential data like machine translation and question answering.
The document provides an overview of LSTM (Long Short-Term Memory) networks. It first reviews RNNs (Recurrent Neural Networks) and their limitations in capturing long-term dependencies. It then introduces LSTM networks, which address this issue using forget, input, and output gates that allow the network to retain information for longer. Code examples are provided to demonstrate how LSTM remembers information over many time steps. Resources for further reading on LSTMs and RNNs are listed at the end.
Anomaly detection (or Outlier analysis) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. It is used is applications such as intrusion detection, fraud detection, fault detection and monitoring processes in various domains including energy, healthcare and finance. In this talk, we will introduce anomaly detection and discuss the various analytical and machine learning techniques used in in this field. Through a case study, we will discuss how anomaly detection techniques could be applied to energy data sets. We will also demonstrate, using R and Apache Spark, an application to help reinforce concepts in anomaly detection and best practices in analyzing and reviewing results.
Human Activity Recognition (HAR) systems aim to recognize human activities through sensors in order to provide assistance. The key steps in designing a HAR system are:
1) Acquiring sensor data and preprocessing it by removing noise.
2) Segmenting the preprocessed data into windows that may contain activities.
3) Extracting features from each window to reduce the data into discriminative features.
4) Training a classification model on the extracted features to predict activity labels, and evaluating the model's performance using methods like a confusion matrix.
Genetic algorithms are a type of artificial intelligence search technique inspired by natural selection. They work by randomly generating an initial population of solutions, evaluating their fitness, then breeding new solutions through selection, crossover and mutation over many generations until an optimal solution is found. Some key steps include randomly initializing a population, determining fitness, selecting parents, performing crossover on parents to create new solutions, mutating new solutions, determining fitness of new population, and repeating until a stopping criteria is met such as a good enough solution being found. Genetic algorithms have been applied to many optimization and search problems across various domains.
KNN Algorithm - How KNN Algorithm Works With Example | Data Science For Begin...Simplilearn
This K-Nearest Neighbor Classification Algorithm presentation (KNN Algorithm) will help you understand what is KNN, why do we need KNN, how do we choose the factor 'K', when do we use KNN, how does KNN algorithm work and you will also see a use case demo showing how to predict whether a person will have diabetes or not using KNN algorithm. KNN algorithm can be applied to both classification and regression problems. Apparently, within the Data Science industry, it's more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. Now lets deep dive into these slides to understand what is KNN algorithm and how does it actually works.
Below topics are explained in this K-Nearest Neighbor Classification Algorithm (KNN Algorithm) tutorial:
1. Why do we need KNN?
2. What is KNN?
3. How do we choose the factor 'K'?
4. When do we use KNN?
5. How does KNN algorithm work?
6. Use case - Predict whether a person will have diabetes or not
Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer
Why learn Machine Learning?
Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
Learn more at: https://www.simplilearn.com
Introduction to Recurrent Neural NetworkKnoldus Inc.
The document provides an introduction to recurrent neural networks (RNNs). It discusses how RNNs differ from feedforward neural networks in that they have internal memory and can use their output from the previous time step as input. This allows RNNs to process sequential data like time series. The document outlines some common RNN types and explains the vanishing gradient problem that can occur in RNNs due to multiplication of small gradient values over many time steps. It discusses solutions to this problem like LSTMs and techniques like weight initialization and gradient clipping.
The document discusses Long Short Term Memory (LSTM) networks, which are a type of recurrent neural network capable of learning long-term dependencies. It explains that unlike standard RNNs, LSTMs use forget, input, and output gates to control the flow of information into and out of the cell state, allowing them to better capture long-range temporal dependencies in sequential data like text, audio, and time-series data. The document provides details on how LSTM gates work and how LSTMs can be used for applications involving sequential data like machine translation and question answering.
The document provides an overview of LSTM (Long Short-Term Memory) networks. It first reviews RNNs (Recurrent Neural Networks) and their limitations in capturing long-term dependencies. It then introduces LSTM networks, which address this issue using forget, input, and output gates that allow the network to retain information for longer. Code examples are provided to demonstrate how LSTM remembers information over many time steps. Resources for further reading on LSTMs and RNNs are listed at the end.
Anomaly detection (or Outlier analysis) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. It is used is applications such as intrusion detection, fraud detection, fault detection and monitoring processes in various domains including energy, healthcare and finance. In this talk, we will introduce anomaly detection and discuss the various analytical and machine learning techniques used in in this field. Through a case study, we will discuss how anomaly detection techniques could be applied to energy data sets. We will also demonstrate, using R and Apache Spark, an application to help reinforce concepts in anomaly detection and best practices in analyzing and reviewing results.
Human Activity Recognition (HAR) systems aim to recognize human activities through sensors in order to provide assistance. The key steps in designing a HAR system are:
1) Acquiring sensor data and preprocessing it by removing noise.
2) Segmenting the preprocessed data into windows that may contain activities.
3) Extracting features from each window to reduce the data into discriminative features.
4) Training a classification model on the extracted features to predict activity labels, and evaluating the model's performance using methods like a confusion matrix.
Genetic algorithms are a type of artificial intelligence search technique inspired by natural selection. They work by randomly generating an initial population of solutions, evaluating their fitness, then breeding new solutions through selection, crossover and mutation over many generations until an optimal solution is found. Some key steps include randomly initializing a population, determining fitness, selecting parents, performing crossover on parents to create new solutions, mutating new solutions, determining fitness of new population, and repeating until a stopping criteria is met such as a good enough solution being found. Genetic algorithms have been applied to many optimization and search problems across various domains.
KNN Algorithm - How KNN Algorithm Works With Example | Data Science For Begin...Simplilearn
This K-Nearest Neighbor Classification Algorithm presentation (KNN Algorithm) will help you understand what is KNN, why do we need KNN, how do we choose the factor 'K', when do we use KNN, how does KNN algorithm work and you will also see a use case demo showing how to predict whether a person will have diabetes or not using KNN algorithm. KNN algorithm can be applied to both classification and regression problems. Apparently, within the Data Science industry, it's more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. Now lets deep dive into these slides to understand what is KNN algorithm and how does it actually works.
Below topics are explained in this K-Nearest Neighbor Classification Algorithm (KNN Algorithm) tutorial:
1. Why do we need KNN?
2. What is KNN?
3. How do we choose the factor 'K'?
4. When do we use KNN?
5. How does KNN algorithm work?
6. Use case - Predict whether a person will have diabetes or not
Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer
Why learn Machine Learning?
Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
Learn more at: https://www.simplilearn.com
Introduction to Recurrent Neural NetworkKnoldus Inc.
The document provides an introduction to recurrent neural networks (RNNs). It discusses how RNNs differ from feedforward neural networks in that they have internal memory and can use their output from the previous time step as input. This allows RNNs to process sequential data like time series. The document outlines some common RNN types and explains the vanishing gradient problem that can occur in RNNs due to multiplication of small gradient values over many time steps. It discusses solutions to this problem like LSTMs and techniques like weight initialization and gradient clipping.
This document discusses unsupervised machine learning classification through clustering. It defines clustering as the process of grouping similar items together, with high intra-cluster similarity and low inter-cluster similarity. The document outlines common clustering algorithms like K-means and hierarchical clustering, and describes how K-means works by assigning points to centroids and iteratively updating centroids. It also discusses applications of clustering in domains like marketing, astronomy, genomics and more.
Anomaly detection techniques aim to identify outliers or anomalies in datasets. Statistical approaches assume a data distribution and use tests to detect outliers. Distance-based approaches represent data as vectors and use nearest neighbors, densities, or clustering to identify anomalies. Model-based approaches build profiles of normal behavior and detect anomalies as observations differing significantly from normal profiles. Key challenges are determining the number of outliers, handling unlabeled data, and detecting anomalies as needles in haystacks of normal data.
How to validate a model?
What is a best model ?
Types of data
Types of errors
The problem of over fitting
The problem of under fitting
Bias Variance Tradeoff
Cross validation
K-Fold Cross validation
Boot strap Cross validation
Anomaly/Novelty detection with scikit-learnagramfort
This document discusses anomaly detection techniques in scikit-learn. It begins by defining anomalies and outliers, then describes the main types of anomaly detection as supervised, semi-supervised (novelty detection), and unsupervised. Popular density-based, kernel, nearest neighbors, and tree/partitioning approaches are covered. Examples are given using Gaussian mixture models, one-class SVM, local outlier factor, and isolation forest algorithms. Challenges in anomaly detection like parameter tuning and evaluation are also noted.
Hierarchical Clustering | Hierarchical Clustering in R |Hierarchical Clusteri...Simplilearn
This presentation about hierarchical clustering will help you understand what is clustering, what is hierarchical clustering, how does hierarchical clustering work, what is distance measure, what is agglomerative clustering, what is divisive clustering and you will also see a demo on how to group states based on their sales using clustering method. Clustering is the method of dividing the objects into clusters which are similar between them and are dissimilar to the objects belonging to another cluster. It is used to find data clusters such that each cluster has the most closely matched data. Prototype-based clustering, hierarchical clustering, and density-based clustering are the three types of clustering algorithms. Lets us discuss hierarchical clustering in this video. In simple terms, Hierarchical clustering is separating data into different groups based on some measure of similarity.
Below topics are explained in this "Hierarchical Clustering" presentation:
1. What is clustering?
2. What is hierarchical clustering
3. How hierarchical clustering works?
4. Distance measure
5. What is agglomerative clustering
6. What is divisive clustering
7. Demo: to group states based on their sales
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at www.simplilearn.com
RNN AND LSTM
This document provides an overview of RNNs and LSTMs:
1. RNNs can process sequential data like time series data using internal hidden states.
2. LSTMs are a type of RNN that use memory cells to store information for long periods of time.
3. LSTMs have input, forget, and output gates that control information flow into and out of the memory cell.
Support vector machines are a type of supervised machine learning algorithm used for classification and regression analysis. They work by mapping data to high-dimensional feature spaces to find optimal linear separations between classes. Key advantages are effectiveness in high dimensions, memory efficiency using support vectors, and versatility through kernel functions. Hyperparameters like kernel type, gamma, and C must be tuned for best performance. Common kernels include linear, polynomial, and radial basis function kernels.
You will learn the basic concepts of machine learning classification and will be introduced to some different algorithms that can be used. This is from a very high level and will not be getting into the nitty-gritty details.
Team 7 presented their findings on anomaly detection in credit card transactions. They tested several techniques including random forests with SMOTE oversampling, one class SVMs, and threshold tuning to optimize AUC. Their best model predicted 98% of fraud transactions while maintaining high precision and recall. They demonstrated their model on a live credit card fraud detection system and for analyzing single transactions.
Time series analysis involves collecting past observations of a variable to develop a model that can be used to forecast future values. The basic idea is that history can predict the future. A time series typically contains trend, seasonal, cyclical, and irregular components. Common time series models include exponential smoothing, Holt-Winters, and ARIMA. Exponential smoothing assigns more weight to recent observations. Holt-Winters extends exponential smoothing to account for trend and seasonality. ARIMA models past values and errors to forecast the future. Determining the appropriate ARIMA model requires identifying the degree of differencing needed to make the time series stationary.
Independent Component Analysis (ICA) is a statistical technique used to separate mixed signals into their independent source components. ICA assumes that the observed mixed data was generated by mixing together statistically independent source signals. ICA uses an "unmixing" matrix to separate the mixed signals by maximizing the statistical independence of the estimated components, with the goal of recovering the original independent source signals. ICA models the probability distribution of each independent source signal using the sigmoid function, and then iteratively updates the unmixing matrix weights to maximize the overall likelihood of the data, until convergence is reached. However, ICA has limitations in that the original source signal order and scaling cannot be determined if the source signals are Gaussian distributed.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
TensorFlow Tutorial | Deep Learning With TensorFlow | TensorFlow Tutorial For...Simplilearn
The document provides an overview of using TensorFlow to build deep learning models. It discusses how TensorFlow uses computational graphs to process data and perform computations. Tensors represent multi-dimensional data and are core to TensorFlow's operations. The document also demonstrates how to build simple models like linear regression and recurrent neural networks (RNNs) using TensorFlow. An example RNN model predicts monthly milk production using time series data.
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
The document discusses Generative Adversarial Networks (GANs), a type of generative model proposed by Ian Goodfellow in 2014. GANs use two neural networks, a generator and discriminator, that compete against each other. The generator produces synthetic data to fool the discriminator, while the discriminator learns to distinguish real from synthetic data. GANs have been used successfully to generate realistic images when trained on large datasets. Examples mentioned include Pix2Pix for image-to-image translation and STACKGAN for text-to-image generation.
The document provides an overview of Long Short Term Memory (LSTM) networks. It discusses:
1) The vanishing gradient problem in traditional RNNs and how LSTMs address it through gated cells that allow information to persist without decay.
2) The key components of LSTMs - forget gates, input gates, output gates and cell states - and how they control the flow of information.
3) Common variations of LSTMs including peephole connections, coupled forget/input gates, and Gated Recurrent Units (GRUs). Applications of LSTMs in areas like speech recognition, machine translation and more are also mentioned.
Clustering algorithms are used to group similar data points together. K-means clustering aims to partition data into k clusters by minimizing distances between data points and cluster centers. Hierarchical clustering builds nested clusters by merging or splitting clusters based on distance metrics. Density-based clustering identifies clusters as areas of high density separated by areas of low density, like DBScan which uses parameters of minimum points and epsilon distance.
Learning a nonlinear embedding by preserving class neibourhood structure 최종WooSung Choi
Salakhutdinov, Ruslan, and Geoffrey E. Hinton. "Learning a nonlinear embedding by preserving class neighbourhood structure." International Conference on Artificial Intelligence and Statistics. 2007.
This document discusses unsupervised machine learning classification through clustering. It defines clustering as the process of grouping similar items together, with high intra-cluster similarity and low inter-cluster similarity. The document outlines common clustering algorithms like K-means and hierarchical clustering, and describes how K-means works by assigning points to centroids and iteratively updating centroids. It also discusses applications of clustering in domains like marketing, astronomy, genomics and more.
Anomaly detection techniques aim to identify outliers or anomalies in datasets. Statistical approaches assume a data distribution and use tests to detect outliers. Distance-based approaches represent data as vectors and use nearest neighbors, densities, or clustering to identify anomalies. Model-based approaches build profiles of normal behavior and detect anomalies as observations differing significantly from normal profiles. Key challenges are determining the number of outliers, handling unlabeled data, and detecting anomalies as needles in haystacks of normal data.
How to validate a model?
What is a best model ?
Types of data
Types of errors
The problem of over fitting
The problem of under fitting
Bias Variance Tradeoff
Cross validation
K-Fold Cross validation
Boot strap Cross validation
Anomaly/Novelty detection with scikit-learnagramfort
This document discusses anomaly detection techniques in scikit-learn. It begins by defining anomalies and outliers, then describes the main types of anomaly detection as supervised, semi-supervised (novelty detection), and unsupervised. Popular density-based, kernel, nearest neighbors, and tree/partitioning approaches are covered. Examples are given using Gaussian mixture models, one-class SVM, local outlier factor, and isolation forest algorithms. Challenges in anomaly detection like parameter tuning and evaluation are also noted.
Hierarchical Clustering | Hierarchical Clustering in R |Hierarchical Clusteri...Simplilearn
This presentation about hierarchical clustering will help you understand what is clustering, what is hierarchical clustering, how does hierarchical clustering work, what is distance measure, what is agglomerative clustering, what is divisive clustering and you will also see a demo on how to group states based on their sales using clustering method. Clustering is the method of dividing the objects into clusters which are similar between them and are dissimilar to the objects belonging to another cluster. It is used to find data clusters such that each cluster has the most closely matched data. Prototype-based clustering, hierarchical clustering, and density-based clustering are the three types of clustering algorithms. Lets us discuss hierarchical clustering in this video. In simple terms, Hierarchical clustering is separating data into different groups based on some measure of similarity.
Below topics are explained in this "Hierarchical Clustering" presentation:
1. What is clustering?
2. What is hierarchical clustering
3. How hierarchical clustering works?
4. Distance measure
5. What is agglomerative clustering
6. What is divisive clustering
7. Demo: to group states based on their sales
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at www.simplilearn.com
RNN AND LSTM
This document provides an overview of RNNs and LSTMs:
1. RNNs can process sequential data like time series data using internal hidden states.
2. LSTMs are a type of RNN that use memory cells to store information for long periods of time.
3. LSTMs have input, forget, and output gates that control information flow into and out of the memory cell.
Support vector machines are a type of supervised machine learning algorithm used for classification and regression analysis. They work by mapping data to high-dimensional feature spaces to find optimal linear separations between classes. Key advantages are effectiveness in high dimensions, memory efficiency using support vectors, and versatility through kernel functions. Hyperparameters like kernel type, gamma, and C must be tuned for best performance. Common kernels include linear, polynomial, and radial basis function kernels.
You will learn the basic concepts of machine learning classification and will be introduced to some different algorithms that can be used. This is from a very high level and will not be getting into the nitty-gritty details.
Team 7 presented their findings on anomaly detection in credit card transactions. They tested several techniques including random forests with SMOTE oversampling, one class SVMs, and threshold tuning to optimize AUC. Their best model predicted 98% of fraud transactions while maintaining high precision and recall. They demonstrated their model on a live credit card fraud detection system and for analyzing single transactions.
Time series analysis involves collecting past observations of a variable to develop a model that can be used to forecast future values. The basic idea is that history can predict the future. A time series typically contains trend, seasonal, cyclical, and irregular components. Common time series models include exponential smoothing, Holt-Winters, and ARIMA. Exponential smoothing assigns more weight to recent observations. Holt-Winters extends exponential smoothing to account for trend and seasonality. ARIMA models past values and errors to forecast the future. Determining the appropriate ARIMA model requires identifying the degree of differencing needed to make the time series stationary.
Independent Component Analysis (ICA) is a statistical technique used to separate mixed signals into their independent source components. ICA assumes that the observed mixed data was generated by mixing together statistically independent source signals. ICA uses an "unmixing" matrix to separate the mixed signals by maximizing the statistical independence of the estimated components, with the goal of recovering the original independent source signals. ICA models the probability distribution of each independent source signal using the sigmoid function, and then iteratively updates the unmixing matrix weights to maximize the overall likelihood of the data, until convergence is reached. However, ICA has limitations in that the original source signal order and scaling cannot be determined if the source signals are Gaussian distributed.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
TensorFlow Tutorial | Deep Learning With TensorFlow | TensorFlow Tutorial For...Simplilearn
The document provides an overview of using TensorFlow to build deep learning models. It discusses how TensorFlow uses computational graphs to process data and perform computations. Tensors represent multi-dimensional data and are core to TensorFlow's operations. The document also demonstrates how to build simple models like linear regression and recurrent neural networks (RNNs) using TensorFlow. An example RNN model predicts monthly milk production using time series data.
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
The document discusses Generative Adversarial Networks (GANs), a type of generative model proposed by Ian Goodfellow in 2014. GANs use two neural networks, a generator and discriminator, that compete against each other. The generator produces synthetic data to fool the discriminator, while the discriminator learns to distinguish real from synthetic data. GANs have been used successfully to generate realistic images when trained on large datasets. Examples mentioned include Pix2Pix for image-to-image translation and STACKGAN for text-to-image generation.
The document provides an overview of Long Short Term Memory (LSTM) networks. It discusses:
1) The vanishing gradient problem in traditional RNNs and how LSTMs address it through gated cells that allow information to persist without decay.
2) The key components of LSTMs - forget gates, input gates, output gates and cell states - and how they control the flow of information.
3) Common variations of LSTMs including peephole connections, coupled forget/input gates, and Gated Recurrent Units (GRUs). Applications of LSTMs in areas like speech recognition, machine translation and more are also mentioned.
Clustering algorithms are used to group similar data points together. K-means clustering aims to partition data into k clusters by minimizing distances between data points and cluster centers. Hierarchical clustering builds nested clusters by merging or splitting clusters based on distance metrics. Density-based clustering identifies clusters as areas of high density separated by areas of low density, like DBScan which uses parameters of minimum points and epsilon distance.
Learning a nonlinear embedding by preserving class neibourhood structure 최종WooSung Choi
Salakhutdinov, Ruslan, and Geoffrey E. Hinton. "Learning a nonlinear embedding by preserving class neighbourhood structure." International Conference on Artificial Intelligence and Statistics. 2007.
tIt appears that you've provided a set of instructions or input format for a machine learning task, particularly clustering using K-Means. Let's break down what each component means:
(number of clusters):
This is a placeholder for an actual numerical value that represents the desired number of clusters into which you want to divide your training data. In K-Means clustering, you need to specify in advance how many clusters (K) you want the algorithm to find in your data.
Training set:
The "training set" is your dataset, which contains the data points that you want to cluster. Each data point represents an observation or sample in your dataset.
(drop convention):
It's not clear from this input what "(drop convention)" refers to. It could be related to a specific data preprocessing or handling instruction, but without additional context or information, it's challenging to provide a precise explanation for this part.
In summary, you are expected to provide the number of clusters (K) that you want to discover in your training data, and the training data itself contains the observations or samples that will be used for clustering. The "(drop convention)" part may require further clarification or context to provide a meaningful explanation.Clustering is a fundamental concept in the field of machine learning and data analysis that involves grouping similar data points together based on certain criteria or patterns. It is a technique used to discover inherent structures, relationships, or similarities within a dataset when there are no predefined labels or categories. Clustering is widely employed in various domains, including marketing, biology, image analysis, recommendation systems, and more. In this comprehensive explanation of clustering, we will explore its principles, methods, applications, and key considerations.
Table of Contents
Introduction to Clustering
Key Concepts and Terminology
Types of Clustering
3.1. Partitioning Clustering
3.2. Hierarchical Clustering
3.3. Density-Based Clustering
3.4. Model-Based Clustering
Distance Metrics and Similarity Measures
Common Clustering Algorithms
5.1. K-Means Clustering
5.2. Hierarchical Agglomerative Clustering
5.3. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
5.4. Gaussian Mixture Models (GMM)
Evaluation of Clusters
Applications of Clustering
7.1. Customer Segmentation
7.2. Image Segmentation
7.3. Anomaly Detection
7.4. Document Clustering
7.5. Recommender Systems
7.6. Genomic Clustering
Challenges and Considerations
8.1. Determining the Number of Clusters (K)
8.2. Handling High-Dimensional Data
8.3. Initial Centroid Selection
8.4. Scaling and Normalization
8.5. Interpretation of Results
Best Practices in Clustering
Future Trends and Advances
Conclusion
1. Introduction to Clustering
Clustering, in the context of data analysis and machine learning, refers to the process of grouping a set of data points into subsets,
본 논문에서는 분배형 강화학습(Distributional Reinforcement Learning)에서 벨만 다이내믹스를 통해 확률 분포를 학습하는 문제를 고려합니다. 이전 연구들은 각 반환 분포의 유한 개의 통계량을 신경망을 통해 학습하는 방법을 사용해왔으나, 이 방법은 반환 분포의 함수적 형태에 제한을 받아 제한적인 표현력을 가지며, 미리 정의된 통계량을 유지하는 것이 어려웠습니다. 본 논문에서는 이러한 제한을 없애기 위해 최대 평균 거리(Maximum Mean Discrepancy, MMD)라는 가설 검정 기술을 활용해 반환 분포의 결정론적인(의사 난수를 사용한) 표본들을 학습하는 방법을 제안합니다. 이를 통해 반환 분포와 벨만 타겟 간의 모든 모멘트(순간값)를 암묵적으로 일치시킴으로써 분배형 벨만 연산자의 수렴성을 보장하며, 분포 근사에 대한 유한 샘플 분석을 제시합니다. 실험 결과, 본 논문에서 제안한 방법은 분배형 강화학습의 기본 모델보다 우수한 성능을 보이며, Atari 게임에서 분산형 에이전트를 사용하지 않는 경우에도 최고 성적을 기록합니다.
The document discusses various clustering algorithms and concepts:
1) K-means clustering groups data by minimizing distances between points and cluster centers, but it is sensitive to initialization and may find local optima.
2) K-medians clustering is similar but uses point medians instead of means as cluster representatives.
3) K-center clustering aims to minimize maximum distances between points and clusters, and can be approximated with a farthest-first traversal algorithm.
This document provides an overview of machine learning techniques that can be applied in finance, including exploratory data analysis, clustering, classification, and regression methods. It discusses statistical learning approaches like data mining and modeling. For clustering, it describes techniques like k-means clustering, hierarchical clustering, Gaussian mixture models, and self-organizing maps. For classification, it mentions discriminant analysis, decision trees, neural networks, and support vector machines. It also provides summaries of regression, ensemble methods, and working with big data and distributed learning.
This document summarizes a talk given by Heiko Strathmann on using partial posterior paths to estimate expectations from large datasets without full posterior simulation. The key ideas are:
1. Construct a path of "partial posteriors" by sequentially adding mini-batches of data and computing expectations over these posteriors.
2. "Debias" the path of expectations to obtain an unbiased estimator of the true posterior expectation using a technique from stochastic optimization literature.
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Experiments on synthetic and real-world examples demonstrate competitive performance versus standard M
This document summarizes a research paper that proposes a new method to accelerate the nearest neighbor search step of the k-means clustering algorithm. The k-means algorithm is computationally expensive due to calculating distances between data points and cluster centers. The proposed method uses geometric relationships between data points and centers to reject centers that are unlikely to be the nearest neighbor, without decreasing clustering accuracy. Experimental results showed the method significantly reduced the number of distance computations required.
We consider the problem of finding anomalies in high-dimensional data using popular PCA based anomaly scores. The naive algorithms for computing these scores explicitly compute the PCA of the covariance matrix which uses space quadratic in the dimensionality of the data. We give the first streaming algorithms
that use space that is linear or sublinear in the dimension. We prove general results showing that any sketch of a matrix that satisfies a certain operator norm guarantee can be used to approximate these scores. We instantiate these results with powerful matrix sketching techniques such as Frequent Directions and random projections to derive efficient and practical algorithms for these problems, which we validate over real-world data sets. Our main technical contribution is to prove matrix perturbation
inequalities for operators arising in the computation of these measures.
-Proceedings: https://arxiv.org/abs/1804.03065
-Origin: https://arxiv.org/abs/1804.03065
This document summarizes a distributed cloud-based genetic algorithm framework called TunUp for tuning the parameters of data clustering algorithms. TunUp integrates existing machine learning libraries and implements genetic algorithm techniques to tune parameters like K (number of clusters) and distance measures for K-means clustering. It evaluates internal clustering quality metrics on sample datasets and tunes parameters to optimize a chosen metric like AIC. The document outlines TunUp's features, describes how it implements genetic algorithms and parallelization, and concludes it is an open solution for clustering algorithm evaluation, validation and tuning.
Event classification & prediction using support vector machineRuta Kambli
This document provides an overview of event classification and prediction using support vector machines (SVM). It begins with an introduction to classification, machine learning, and SVM. It then discusses binary classification with SVM, including hard-margin and soft-margin SVM, kernels, and multiclass classification. The document presents case studies on classifying hand movements from electromyography data and predicting power grid blackouts using SVM. It concludes that SVM is effective for these classification tasks and can initiate prevention mechanisms for predicted events.
PyData NYC 2015 - Automatically Detecting Outliers with Datadog Datadog
Monitoring even a modestly-sized systems infrastructure quickly becomes untenable without automated alerting. For many metrics it is nontrivial to define ahead of time what constitutes “normal” versus “abnormal” values. This is especially true for metrics whose baseline value fluctuates over time. To make this problem more tractable, Datadog provides outlier detection functionality to automatically identify any host (or group of hosts) that is behaving abnormally compared to its peers.
These slides cover the algorithms we use for outlier detection, and show how easy they are to implement using Python. This presentation also covers the lessons we've learned from using outlier detection on our own systems, along with some real-life examples on how to avoid false positives and negatives.
Learn more at www.datadoghq.com.
This document discusses different types of clustering analysis techniques in data mining. It describes clustering as the task of grouping similar objects together. The document outlines several key clustering algorithms including k-means clustering and hierarchical clustering. It provides an example to illustrate how k-means clustering works by randomly selecting initial cluster centers and iteratively assigning data points to clusters and recomputing cluster centers until convergence. The document also discusses limitations of k-means and how hierarchical clustering builds nested clusters through sequential merging of clusters based on a similarity measure.
Hierarchical clustering is a method of partitioning a set of data into meaningful sub-classes or clusters. It involves two approaches - agglomerative, which successively links pairs of items or clusters, and divisive, which starts with the whole set as a cluster and divides it into smaller partitions. Agglomerative Nesting (AGNES) is an agglomerative technique that merges clusters with the least dissimilarity at each step, eventually combining all clusters. Divisive Analysis (DIANA) is the inverse, starting with all data in one cluster and splitting it until each data point is its own cluster. Both approaches can be visualized using dendrograms to show the hierarchical merging or splitting of clusters.
Locations are described with feature histograms based on surface orientation and smoothness, and loop closure can be detected by matching feature histograms.
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발표자:
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Kristine Pearce, Principal ML BD, AWS
❻❸❼⓿❽❻❷⓿⓿❼ SATTA MATKA DPBOSS KALYAN MATKA RESULTS KALYAN CHART KALYAN MATKA MATKA RESULT KALYAN MATKA TIPS SATTA MATKA MATKA COM MATKA PANA JODI TODAY
4. Background
• High dimensionality
• Irregular lengths
• Noise and time shifts
time (s)
variable
A time series is a collection of observations made sequentially in time.
15. Which Distance Measure to Use
• Type of the data
• Research questions
Criteria Euclidean DTW
Supports Time Series length differences No Yes
Supports Time Series time shifts No Yes
Computational costs Low High
29. Clustering
Clustering algorithm Distance measure Prototype
Partitional
K – means / K – medoid Euclidean / Manhattan Mean / PAM
TAD Pole DTW DBA
K – shape SBD Shape Extraction
Hierarchical Agglomerative All All
Clustering AlgorithmDistance
Measure
Prototype
N clusters
Time Series
Data
44. DTW
Right combination of distance measure & prototype
Conclusions
Clustering algorithm Distance measure Prototype
Partitional
K – means / K – medoid Euclidean / Manhattan Mean / PAM
TAD Pole DTW DBA
K – shape SBD Shape Extraction
Hierarchical Agglomerative All All
Editor's Notes
Provide quantification for the dissimilarity between two time-series
The classification of objects, into clusters, requires some methods for measuring the distance or the (dis)similarity between the objects
The term proximity is used to refer to either similarity or dissimilarity. Frequently, the term distance is used as a synonym for dissimilarity.
Variable for
Recent years have seen a surge of interest in time series clustering.
Data characteristics are evolving and traditional clustering algorithms are becoming less popular in time series clustering.
The most commonly used distance measures are only defined for series of equal length and are sensitive to noise, scale and time shifts
Thus, many other distance measures tailored to time-series have been developed in order to overcome these limitations; other challenges associated with the structure of time-series, such as multiple variables, serial correlation
each
Goal is to put them all together in clusters
Input in customer segmentation
Mention about chicken segmentation
Behavior based on purchases, bank transactions, energy, other utilities usage/consumption, social networks – who is connected to who
Hierarchy of classes dendrogram
Provide quantification for the dissimilarity between two time-series
The classification of objects, into clusters, requires some methods for measuring the distance or the (dis)similarity between the objects
The term proximity is used to refer to either similarity or dissimilarity. Frequently, the term distance is used as a synonym for dissimilarity.
https://en.wikipedia.org/wiki/Taxicab_geometry
The distance between two points measured along axes at right angles.
Also known as Manhattan length, rectilinear distance, Minkowski's L1 distance, L1 norm, taxi cab metric, snake distance, city block distance
Correlation measures are only useful if/when the relationship between attributes is linear. So if the correlation is 0, then there is no linear relationship between the two data objects.
http://cs.tsu.edu/ghemri/CS497/ClassNotes/ML/Similarity%20Measures.pdf
Be ready to explain pearson and spearman
When time series have different lengths
One of the most used measure of the similarity between two time series
Originally designed to treat automatic speech recognition
Optimal global alignment between two time series, exploiting temporal distortions between them
Designed especially for time series analysis
Ignore shifts in time dimension
Ignore speeds of two time series
How is it calculated?
When time series have different lengths
One of the most used measure of the similarity between two time series
Originally designed to treat automatic speech recognition
Optimal global alignment between two time series, exploiting temporal distortions between them
Designed especially for time series analysis
Ignore shifts in time dimension
Ignore speeds of two time series
How is it calculated?
https://www.datanovia.com/en/lessons/clustering-distance-measures/
For example, correlation-based distance is often used in gene expression data analysis.
Correlation-based distance considers two objects to be similar if their features are highly correlated, even though the observed values may be far apart in terms of Euclidean distance.
For most clustering package, Euclidean is default.
If we want to identify clusters of observations with the same overall profiles regardless of their magnitudes, then correlation-based distance
If correlation, Pearson’s correlation is quite sensitive to outliers
Commonly used in
gene expression data analysis
marketing, if we want to identify group of shoppers with the same preference in term of items, regardless of the volume of items they bought.
Hierarchy of classes dendrogram
Gamma is the optimization function.
A is the alignment function
Hierarchy of classes dendrogram
Hierarchy of classes dendrogram
Clusters are defines beforehand
Compute distance between point and centroids and keep the minimum
Predict For each data point calculate the distance from both centroids and the data point is assigned to the cluster with the min distance
Move centroids in the point where the is the mean distance so that they are in the center of the cluster
Compute distance between point and centroids and keep the minimum
Predict For each data point calculate the distance from both centroids and the data point is assigned to the cluster with the min distance
Move centroids in the point where the is the mean distance so that they are in the center of the cluster
Compute distance between point and centroids and keep the minimum
Predict For each data point calculate the distance from both centroids and the data point is assigned to the cluster with the min distance
Move centroids in the point where the is the mean distance so that they are in the center of the cluster
Hierarchy of classes dendrogram
Each character has each one cluster
Input = genetic code
Selma + Patty twins
Lisa + Merge mother and daughter (less similarity because the share genetic code with Homer Simpson)
Selma + patty sisters of Marge
Number of clusters and order of clustering
A: number of time series assigned to same cluster and belong to the same class
B: number of time series assigned to different cluster and belong to the different class
C: number of time series assigned to different cluster and belong to the same class
D: number of time series assigned to same cluster and belong to the different class