This presentation gives an overview of machine learning, discusses its potential in an industrial setting and presents two industrial applications (energy load forecasting, understanding academic publications) based on the speaker's experience.
Data clustering and optimization techniquesSpyros Ktenas
This document discusses data clustering techniques and algorithms. It describes clustering as the process of separating a set of objects into logical groups based on similarity. Common clustering applications include classification of species, customer segmentation, and grouping search engine results. Popular clustering algorithms mentioned include k-means, hierarchical, distribution-based, and density-based clustering. The document also summarizes several papers that propose optimizations to clustering algorithms like k-means in order to improve accuracy and efficiency. Finally, it notes initial progress on a PHP implementation of the k-means algorithm.
Presentation at Data ScienceTech Institute campuses, Paris and Nice, May 2016 , including Intro, Data Science History and Terms; 10 Real-World Data Science Lessons; Data Science Now: Polls & Trends; Data Science Roles; Data Science Job Trends; and Data Science Future
*What is Machine Learning?
-Definition
-Explanation
*Difference between Machine Learning and Standard Programs
*Machine Learning Models
-Supervised Learning
--Classification
--Regression
-Unsupervised Learning
--Clustering
*AI Evolution
-History of AI
-Neural Networks and Deep Learning
-Simple Neural Network and Deep Neural Network
-Difference between AI, Machine Learning, and Deep Learning
Machine learning is the subfield of computer science that, according to Arthur Samuel in 1959, gives "computers the ability to learn without being explicitly programmed.Evolved from the study of pattern recognition and computational learning theory in artificial intelligence,machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,:2 through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or unfeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach,Optical character recognition (OCR),learning to rank and computer vision.
Machine learning involves programming computers to optimize performance using example data or past experience. It is used when human expertise does not exist, humans cannot explain their expertise, solutions change over time, or solutions need to be adapted to particular cases. Learning builds general models from data to approximate real-world examples. There are several types of machine learning including supervised learning (classification, regression), unsupervised learning (clustering), and reinforcement learning. Machine learning has applications in many domains including retail, finance, manufacturing, medicine, web mining, and more.
Graph clustering involves representing data as a graph where each element is a node and connections between elements are modeled by edge weights. The goal is to cluster nodes such that elements within a cluster are well-connected but have few connections to elements outside the cluster. There are various algorithms for graph clustering, including k-spanning tree, shared nearest neighbor, betweenness centrality, highly connected components, and maximal clique enumeration. These algorithms group the nodes of a graph into clusters based on properties like minimum spanning trees, shared neighbors, centrality, edge connectivity, and maximal cliques. Graph clustering has applications in fields like image segmentation, network analysis, biology, medicine, and astronomy.
This knolx is about an introduction to machine learning, wherein we see the basics of various different algorithms. This knolx isn't a complete intro to ML but can be a good starting point for anyone who wants to start in ML. In the end, we will take a look at the demo wherein we will analyze the FIFA dataset going through the understanding of various data analysis techniques and use an ML algorithm to derive 5 players that are similar to each other.
Data clustering and optimization techniquesSpyros Ktenas
This document discusses data clustering techniques and algorithms. It describes clustering as the process of separating a set of objects into logical groups based on similarity. Common clustering applications include classification of species, customer segmentation, and grouping search engine results. Popular clustering algorithms mentioned include k-means, hierarchical, distribution-based, and density-based clustering. The document also summarizes several papers that propose optimizations to clustering algorithms like k-means in order to improve accuracy and efficiency. Finally, it notes initial progress on a PHP implementation of the k-means algorithm.
Presentation at Data ScienceTech Institute campuses, Paris and Nice, May 2016 , including Intro, Data Science History and Terms; 10 Real-World Data Science Lessons; Data Science Now: Polls & Trends; Data Science Roles; Data Science Job Trends; and Data Science Future
*What is Machine Learning?
-Definition
-Explanation
*Difference between Machine Learning and Standard Programs
*Machine Learning Models
-Supervised Learning
--Classification
--Regression
-Unsupervised Learning
--Clustering
*AI Evolution
-History of AI
-Neural Networks and Deep Learning
-Simple Neural Network and Deep Neural Network
-Difference between AI, Machine Learning, and Deep Learning
Machine learning is the subfield of computer science that, according to Arthur Samuel in 1959, gives "computers the ability to learn without being explicitly programmed.Evolved from the study of pattern recognition and computational learning theory in artificial intelligence,machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,:2 through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or unfeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach,Optical character recognition (OCR),learning to rank and computer vision.
Machine learning involves programming computers to optimize performance using example data or past experience. It is used when human expertise does not exist, humans cannot explain their expertise, solutions change over time, or solutions need to be adapted to particular cases. Learning builds general models from data to approximate real-world examples. There are several types of machine learning including supervised learning (classification, regression), unsupervised learning (clustering), and reinforcement learning. Machine learning has applications in many domains including retail, finance, manufacturing, medicine, web mining, and more.
Graph clustering involves representing data as a graph where each element is a node and connections between elements are modeled by edge weights. The goal is to cluster nodes such that elements within a cluster are well-connected but have few connections to elements outside the cluster. There are various algorithms for graph clustering, including k-spanning tree, shared nearest neighbor, betweenness centrality, highly connected components, and maximal clique enumeration. These algorithms group the nodes of a graph into clusters based on properties like minimum spanning trees, shared neighbors, centrality, edge connectivity, and maximal cliques. Graph clustering has applications in fields like image segmentation, network analysis, biology, medicine, and astronomy.
This knolx is about an introduction to machine learning, wherein we see the basics of various different algorithms. This knolx isn't a complete intro to ML but can be a good starting point for anyone who wants to start in ML. In the end, we will take a look at the demo wherein we will analyze the FIFA dataset going through the understanding of various data analysis techniques and use an ML algorithm to derive 5 players that are similar to each other.
a short introduction about deep learning and how we can use deep neural networks in different biological problems such as protein function prediction and gene expression inference.
The document discusses decision trees and the ID3 algorithm. It provides an overview of decision trees, describing their structure and how they are used for classification. It then explains the ID3 algorithm, which builds decision trees based on entropy and information gain. The key steps of ID3 are outlined, including calculating entropy and information gain to select the best attributes to split the data on at each node. Pros and cons of ID3 are also summarized. An example applying ID3 to classify characters from The Simpsons is shown.
This document discusses machine learning and its types. It defines machine learning as a form of artificial intelligence that enables systems to learn from data rather than through explicit programming. The document outlines the main types of learning as supervised, unsupervised, and reinforcement learning. It provides examples of algorithms used for classification and regression under supervised learning as well as clustering and association under unsupervised learning. The document also lists some applications of machine learning and issues to consider in machine learning.
A small informative presentation on machine learning.
It contains the following topics:
Introduction to ML
Types of Learning
Regression
Classification
Classification vs Regression
Clustering
Decision Tree Learning
Random Forest
True vs False
Positive vs Negative
Linear Regression
Logistic Regression
Application of Machine Learning
Future of Machine Learning
Hrjeet Singh completed a 42-day online industrial training from Internshala located in Gurgaon, India. During the training, Singh learned about machine learning concepts including classification, regression, linear regression, logistic regression, decision trees, and K-means clustering. Singh also completed a project using machine learning classifiers to detect breast cancer by analyzing features of breast cancer patient and normal cells.
Machine learning helps predict behavior and recognize patterns that humans cannot by learning from data without relying on programmed rules. It is an algorithmic approach that differs from statistical modeling which formalizes relationships through mathematical equations. Machine learning is a part of the broader field of artificial intelligence which aims to develop systems that can act and respond intelligently like humans. The machine learning workflow involves collecting and preprocessing data, selecting algorithms, training models, and evaluating performance. Common machine learning algorithms include supervised learning, unsupervised learning, reinforcement learning, and deep learning. Popular tools for machine learning include Python, R, TensorFlow, and Spark.
1. Machine learning is a set of techniques that use data to build models that can make predictions without being explicitly programmed.
2. There are two main types of machine learning: supervised learning, where the model is trained on labeled examples, and unsupervised learning, where the model finds patterns in unlabeled data.
3. Common machine learning algorithms include linear regression, logistic regression, decision trees, support vector machines, naive Bayes, k-nearest neighbors, k-means clustering, and random forests. These can be used for regression, classification, clustering, and dimensionality reduction.
- Machine learning is a subset of artificial intelligence that uses data and experience to allow machines to learn and improve. There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.
- Supervised learning uses labeled training data to teach a model. Examples include speech recognition and weather prediction applications. Unsupervised learning discovers patterns in unlabeled data through techniques like clustering and association. Logistic regression and linear regression are common supervised learning algorithms.
This document provides an introduction to machine learning, including definitions, key concepts, and algorithms. It defines machine learning as giving computers the ability to learn without being explicitly programmed. It distinguishes machine learning from artificial intelligence and describes supervised and unsupervised learning. Popular machine learning algorithms like naive Bayes, support vector machines, and decision trees are introduced. Python libraries for machine learning like scikit-learn are also mentioned.
This document summarizes a presentation on machine learning models, adversarial attacks, and defense strategies. It discusses adversarial attacks on machine learning systems, including GAN-based attacks. It then covers various defense strategies against adversarial attacks, such as filter-based adaptive defenses and outlier-based defenses. The presentation also addresses issues around bias in AI systems and the need for explainable and accountable AI.
The document discusses automated machine learning (AutoML). It defines AutoML as providing methods to make machine learning more efficient and accessible to non-machine learning experts. AutoML aims to automate tasks like data preprocessing, feature engineering, algorithm selection and hyperparameter optimization. This can reduce costs, increase productivity for data scientists and democratize machine learning. The document also lists several AutoML tools that provide hyperparameter tuning, full pipeline optimization or neural architecture search.
Big Data Helsinki v 3 | "Federated Learning and Privacy-preserving AI" - Oguz...Dataconomy Media
"Machine learning algorithms require significant amounts of training data which has been centralized on one machine or in a datacenter so far. For numerous applications, such need of collecting data can be extremely privacy-invasive. Recent advancements in AI research approach this issue by a new paradigm of training AI models, i.e., Federated Learning.
In federated learning, edge devices (phones, computers, cars etc.) collaboratively learn a shared AI model while keeping all the training data on device, decoupling the ability to do machine learning from the need to store the data in the cloud. From personal data perspective, this paradigm enables a way of training a model on the device without directly inspecting users’ data on a server. This talk will pinpoint several examples of AI applications benefiting from federated learning and the likely future of privacy-aware systems."
Breast Cancer Detection with Convolutional Neural Networks (CNN)Mehmet Çağrı Aksoy
Photos and various addresses are taken from the internet. It may be subject to copyright.
For references:
https://github.com/mcagriaksoy/EEM-305-Signals-and-Systems
https://medium.com/intro-to-artificial-intelligence/deep-learning-series-1-intro-to-deep-learning-abb1780ee20
Hybrid Technique for Associative Classification of Heart DiseasesJagdeep Singh Malhi
The document discusses a hybrid technique for associative classification of heart diseases using data mining. It summarizes existing classification and association rule mining algorithms applied to heart disease data. The author aims to improve accuracy by generating classification association rules efficiently and integrating classification with association rule mining. The proposed approach is implemented in Weka to extract rules from a heart disease dataset using Apriori and FP-Growth algorithms. The rules are used to classify patients and evaluate the performance compared to other methods.
In this tutorial, we will learn the the following topics -
+ The Curse of Dimensionality
+ Main Approaches for Dimensionality Reduction
+ PCA - Principal Component Analysis
+ Kernel PCA
+ LLE
+ Other Dimensionality Reduction Techniques
This document provides an introduction to machine learning from Syed Nauyan Rashid. It defines machine learning as a field that allows computers to learn without being explicitly programmed. It discusses major machine learning applications such as surveillance, self-driving cars, and medical diagnosis. It also outlines machine learning subtypes including supervised learning, unsupervised learning, and reinforcement learning. Finally, it provides an overview of the machine learning workflow and some helpful resources for learning more about the topic.
Big Data & Machine Learning - TDC2013 São Paulo - 12/0713Mathieu DESPRIEE
Machine learning and big data technologies enable new types of data analysis. Hadoop is an open-source framework that allows distributed storage and processing of large datasets across clusters of computers. It includes tools for working with structured and unstructured data to power applications in areas like recommendations, customer churn prediction, and more.
Big Data & Machine Learning - TDC2013 Sao PauloOCTO Technology
BigData and Machine Learning: Usage and Opportunities for your IT department
Talk presented at The Developer Conference in São Paulo - 12/0713
Mathieu DESPRIEE
a short introduction about deep learning and how we can use deep neural networks in different biological problems such as protein function prediction and gene expression inference.
The document discusses decision trees and the ID3 algorithm. It provides an overview of decision trees, describing their structure and how they are used for classification. It then explains the ID3 algorithm, which builds decision trees based on entropy and information gain. The key steps of ID3 are outlined, including calculating entropy and information gain to select the best attributes to split the data on at each node. Pros and cons of ID3 are also summarized. An example applying ID3 to classify characters from The Simpsons is shown.
This document discusses machine learning and its types. It defines machine learning as a form of artificial intelligence that enables systems to learn from data rather than through explicit programming. The document outlines the main types of learning as supervised, unsupervised, and reinforcement learning. It provides examples of algorithms used for classification and regression under supervised learning as well as clustering and association under unsupervised learning. The document also lists some applications of machine learning and issues to consider in machine learning.
A small informative presentation on machine learning.
It contains the following topics:
Introduction to ML
Types of Learning
Regression
Classification
Classification vs Regression
Clustering
Decision Tree Learning
Random Forest
True vs False
Positive vs Negative
Linear Regression
Logistic Regression
Application of Machine Learning
Future of Machine Learning
Hrjeet Singh completed a 42-day online industrial training from Internshala located in Gurgaon, India. During the training, Singh learned about machine learning concepts including classification, regression, linear regression, logistic regression, decision trees, and K-means clustering. Singh also completed a project using machine learning classifiers to detect breast cancer by analyzing features of breast cancer patient and normal cells.
Machine learning helps predict behavior and recognize patterns that humans cannot by learning from data without relying on programmed rules. It is an algorithmic approach that differs from statistical modeling which formalizes relationships through mathematical equations. Machine learning is a part of the broader field of artificial intelligence which aims to develop systems that can act and respond intelligently like humans. The machine learning workflow involves collecting and preprocessing data, selecting algorithms, training models, and evaluating performance. Common machine learning algorithms include supervised learning, unsupervised learning, reinforcement learning, and deep learning. Popular tools for machine learning include Python, R, TensorFlow, and Spark.
1. Machine learning is a set of techniques that use data to build models that can make predictions without being explicitly programmed.
2. There are two main types of machine learning: supervised learning, where the model is trained on labeled examples, and unsupervised learning, where the model finds patterns in unlabeled data.
3. Common machine learning algorithms include linear regression, logistic regression, decision trees, support vector machines, naive Bayes, k-nearest neighbors, k-means clustering, and random forests. These can be used for regression, classification, clustering, and dimensionality reduction.
- Machine learning is a subset of artificial intelligence that uses data and experience to allow machines to learn and improve. There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.
- Supervised learning uses labeled training data to teach a model. Examples include speech recognition and weather prediction applications. Unsupervised learning discovers patterns in unlabeled data through techniques like clustering and association. Logistic regression and linear regression are common supervised learning algorithms.
This document provides an introduction to machine learning, including definitions, key concepts, and algorithms. It defines machine learning as giving computers the ability to learn without being explicitly programmed. It distinguishes machine learning from artificial intelligence and describes supervised and unsupervised learning. Popular machine learning algorithms like naive Bayes, support vector machines, and decision trees are introduced. Python libraries for machine learning like scikit-learn are also mentioned.
This document summarizes a presentation on machine learning models, adversarial attacks, and defense strategies. It discusses adversarial attacks on machine learning systems, including GAN-based attacks. It then covers various defense strategies against adversarial attacks, such as filter-based adaptive defenses and outlier-based defenses. The presentation also addresses issues around bias in AI systems and the need for explainable and accountable AI.
The document discusses automated machine learning (AutoML). It defines AutoML as providing methods to make machine learning more efficient and accessible to non-machine learning experts. AutoML aims to automate tasks like data preprocessing, feature engineering, algorithm selection and hyperparameter optimization. This can reduce costs, increase productivity for data scientists and democratize machine learning. The document also lists several AutoML tools that provide hyperparameter tuning, full pipeline optimization or neural architecture search.
Big Data Helsinki v 3 | "Federated Learning and Privacy-preserving AI" - Oguz...Dataconomy Media
"Machine learning algorithms require significant amounts of training data which has been centralized on one machine or in a datacenter so far. For numerous applications, such need of collecting data can be extremely privacy-invasive. Recent advancements in AI research approach this issue by a new paradigm of training AI models, i.e., Federated Learning.
In federated learning, edge devices (phones, computers, cars etc.) collaboratively learn a shared AI model while keeping all the training data on device, decoupling the ability to do machine learning from the need to store the data in the cloud. From personal data perspective, this paradigm enables a way of training a model on the device without directly inspecting users’ data on a server. This talk will pinpoint several examples of AI applications benefiting from federated learning and the likely future of privacy-aware systems."
Breast Cancer Detection with Convolutional Neural Networks (CNN)Mehmet Çağrı Aksoy
Photos and various addresses are taken from the internet. It may be subject to copyright.
For references:
https://github.com/mcagriaksoy/EEM-305-Signals-and-Systems
https://medium.com/intro-to-artificial-intelligence/deep-learning-series-1-intro-to-deep-learning-abb1780ee20
Hybrid Technique for Associative Classification of Heart DiseasesJagdeep Singh Malhi
The document discusses a hybrid technique for associative classification of heart diseases using data mining. It summarizes existing classification and association rule mining algorithms applied to heart disease data. The author aims to improve accuracy by generating classification association rules efficiently and integrating classification with association rule mining. The proposed approach is implemented in Weka to extract rules from a heart disease dataset using Apriori and FP-Growth algorithms. The rules are used to classify patients and evaluate the performance compared to other methods.
In this tutorial, we will learn the the following topics -
+ The Curse of Dimensionality
+ Main Approaches for Dimensionality Reduction
+ PCA - Principal Component Analysis
+ Kernel PCA
+ LLE
+ Other Dimensionality Reduction Techniques
This document provides an introduction to machine learning from Syed Nauyan Rashid. It defines machine learning as a field that allows computers to learn without being explicitly programmed. It discusses major machine learning applications such as surveillance, self-driving cars, and medical diagnosis. It also outlines machine learning subtypes including supervised learning, unsupervised learning, and reinforcement learning. Finally, it provides an overview of the machine learning workflow and some helpful resources for learning more about the topic.
Big Data & Machine Learning - TDC2013 São Paulo - 12/0713Mathieu DESPRIEE
Machine learning and big data technologies enable new types of data analysis. Hadoop is an open-source framework that allows distributed storage and processing of large datasets across clusters of computers. It includes tools for working with structured and unstructured data to power applications in areas like recommendations, customer churn prediction, and more.
Big Data & Machine Learning - TDC2013 Sao PauloOCTO Technology
BigData and Machine Learning: Usage and Opportunities for your IT department
Talk presented at The Developer Conference in São Paulo - 12/0713
Mathieu DESPRIEE
An Ecosystem Approach to Artificial IntelligenceAlex Liu
Dr. Alex Liu proposes an ecosystem approach to artificial intelligence to address high failure rates of AI projects. An AI ecosystem has three elements: 1) a data portal to share raw and preprocessed data, 2) a computing platform to share expertise and automate tasks, and 3) a data scientist community to collaborate. By bringing together data, algorithms, scientists, and organizations in an orchestrated way, an AI ecosystem can deliver more value than any individual entity alone.
DSCI 552 machine learning for data sciencepavithrak2205
This document provides an overview of machine learning concepts that will be covered in the DSCI 552 course. It introduces key machine learning tasks like classification, regression, and clustering. It discusses the machine learning process and importance of inductive bias. Example applications in areas like credit scoring, face recognition, and customer segmentation are provided. The reading schedule and grading policy are also outlined.
Dr. James C. Spohrer discusses software convergence and how software is progressing at least as fast as hardware. He cites a study showing a 43 million-fold improvement in solving an optimization problem from 1988-2003, with faster processors accounting for a 1,000-fold improvement and better algorithms accounting for a 43,000-fold improvement embedded in software. Spohrer also discusses emerging technologies and trends like cognitive computing, smart cities, the internet of things, and how they are examples of software convergence across different domains that will transform business and society.
This document provides an overview of machine learning through a case study submitted by computer science students. It discusses the history and evolution of machine learning from its early development in the 1940s-50s to major advances in the 21st century. The document also defines key machine learning terms, describes the typical machine learning process and steps involved, and lists different types of machine learning problems and algorithms. It aims to give readers a comprehensive introduction to the field of machine learning.
Intro/Overview on Machine Learning PresentationAnkit Gupta
This document provides an overview of a presentation on machine learning given at Gurukul Kangri University in 2017. It defines machine learning as a field that allows computers to learn without being explicitly programmed. It discusses different machine learning algorithms including supervised learning, unsupervised learning, and semi-supervised learning. Examples of applications of machine learning discussed include data mining, natural language processing, image recognition, and expert systems. The document also contrasts artificial intelligence, machine learning, and deep learning.
VERGE 23: A Practical Guide to Harnessing AI for DecarbonizationGreenBiz Group
This document provides an overview of a tutorial on harnessing AI for decarbonization. It discusses how AI can help address climate change through various applications like optimizing complex systems, improving predictions, accelerating scientific discovery, and approximating simulations. It also covers key considerations for applying AI to climate problems, like ensuring diverse stakeholder input and that data is representative. Finally, it lists some emerging areas of AI like machine learning, computer vision, and natural language processing that are relevant to decarbonization efforts.
The document discusses AI and digitization. It notes that AI is transforming industries like electricity did 100 years ago. Data is described as the new oil. The convergence of technologies like digitization and increased computing power are enabling breakthroughs in machine learning. Three paradigms of machine learning are discussed: supervised learning, unsupervised learning, and reinforcement learning. Examples of projects at Chalmers include using deep learning for diabetes management and machine learning for language processing tasks. Challenges discussed include automation potentially replacing many jobs over the next 20 years and the need to address economic implications of technological change.
Machine Learning On Big Data: Opportunities And Challenges- Future Research D...PhD Assistance
Machine Learning (ML) is rapidly used in a variety of applications. It has risen to prominence in recent years, owing in part to the emergence of big data. When it comes to big data, ML algorithms have never been more promising. Big data allows machine learning algorithms to discover finer-grained patterns and make more timely and precise predictions than ever before; however, it also poses significant challenges to machine learning, such as model scalability and distributed computing.
Learn More: https://bit.ly/2RB1buD
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Artificial intelligence and machine learning can help analyze large amounts of environmental data to better understand climate change and predict future impacts. AI is used to identify patterns in data from sensors monitoring conditions around the world. This data provides insights into vulnerabilities and helps predict extreme weather events. AI technologies can also optimize renewable energy production and design more energy efficient systems, buildings and consumer products to mitigate climate change. However, training AI models also contributes to carbon emissions which must be addressed.
ANALYSIS OF SYSTEM ON CHIP DESIGN USING ARTIFICIAL INTELLIGENCEijesajournal
Automation is a powerful word that lies everywhere. It shows that without automation, application will not
get developed. In a semiconductor industry, artificial intelligence played a vital role for implementing the
chip based design through automation .The main advantage of applying the machine learning & deep
learning technique is to improve the implementation rate based upon the capability of the society. The
main objective of the proposed system is to apply the deep learning using data driven approach for
controlling the system. Thus leads to a improvement in design, delay ,speed of operation & costs.
Through this system, huge volume of data’s that are generated by the system will also get control.
ANALYSIS OF SYSTEM ON CHIP DESIGN USING ARTIFICIAL INTELLIGENCEijesajournal
Automation is a powerful word that lies everywhere. It shows that without automation, application will not
get developed. In a semiconductor industry, artificial intelligence played a vital role for implementing the
chip based design through automation .The main advantage of applying the machine learning & deep learning technique is to improve the implementation rate based upon the capability of the society. The main objective of the proposed system is to apply the deep learning using data driven approach for controlling the system. Thus leads to a improvement in design, delay ,speed of operation & costs.Through this system, huge volume of data’s that are generated by the system will also get control.
ANALYSIS OF SYSTEM ON CHIP DESIGN USING ARTIFICIAL INTELLIGENCEijesajournal
Automation is a powerful word that lies everywhere. It shows that without automation, application will not get developed. In a semiconductor industry, artificial intelligence played a vital role for implementing the chip based design through automation .The main advantage of applying the machine learning & deep learning technique is to improve the implementation rate based upon the capability of the society. The main objective of the proposed system is to apply the deep learning using data driven approach for controlling the system. Thus leads to a improvement in design, delay ,speed of operation & costs. Through this system, huge volume of data’s that are generated by the system will also get control.
This document provides an introduction to machine learning. It discusses why machine learning is used, such as when human expertise does not exist or changes over time. It explains that machine learning builds general models from example data to approximate patterns in the data. Various applications of machine learning are presented, including retail, finance, manufacturing, and bioinformatics. Supervised learning techniques like classification and regression are covered. Unsupervised learning and reinforcement learning are also introduced. Finally, resources for datasets, journals, and conferences in machine learning are listed.
The document discusses machine learning and data science, providing definitions of data science, applications of data science, the purpose of data science, commonly used machine learning tools, and the components and life cycle of machine learning. It also examines what machine learning is, how it works, the history of machine learning, the differences between machine learning and traditional programming, and examples of supervised and unsupervised learning.
Machine learning involves using algorithms to optimize performance using example data or past experience. It is useful when human expertise does not exist, cannot be explained, or needs to adapt over time. The document discusses different types of machine learning including supervised learning techniques like classification and regression as well as unsupervised learning techniques like clustering. It provides examples of applications in various domains and lists resources for datasets, journals, and conferences in the machine learning field.
An invited talk by Paco Nathan in the speaker series at the University of Chicago's Data Science for Social Good fellowship (2013-08-12) http://dssg.io/2013/05/21/the-fellowship-and-the-fellows.html
Learnings generalized from trends in Data Science:
a 30-year retrospective on Machine Learning,
a 10-year summary of Leading Data Science Teams,
and a 2-year survey of Enterprise Use Cases.
http://www.eventbrite.com/event/7476758185
Machine Learning: Need of Machine Learning, Its Challenges and its ApplicationsArpana Awasthi
BCA Department of JIMS Vasant Kunj-II is one of the best BCA colleges in Delhi NCR. The curriculum is well updated and the subjects included all the latest technologies which are in demand.
JIMS BCA course teaches Python to II semester students and Artificial Intelligence Using Python to Sixth Semester students.
Here is a small article on the Future of Machine Learning, hope you will find it useful.
Machine Learning is a field of Computer science in which computer systems are able to learn from past experiences, examples, environments. With help of various Machine Learning Algorithms, Computers are provided with the ability to sense the data and produce some relevant results.
Machine learning Algorithms provide the technique of predicting the future outcomes or classifying information from the given input to the Machines so that the appropriate decisions can be taken.
This document discusses service systems and their impact on quality of life. It begins by outlining different types of systems that focus on (A) flows of things humans need like transportation and supply chains, (B) human activities like retail, banking, and education, and (C) human governance systems like cities, states, and nations. It then provides more depth on these systems and the disciplines that support them. The document emphasizes that quality of life results from quality of service systems as well as quality jobs and investment opportunities. It concludes by stating the best way to predict the future is to inspire students to build it better.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
Unlocking the mysteries of reproduction: Exploring fecundity and gonadosomati...AbdullaAlAsif1
The pygmy halfbeak Dermogenys colletei, is known for its viviparous nature, this presents an intriguing case of relatively low fecundity, raising questions about potential compensatory reproductive strategies employed by this species. Our study delves into the examination of fecundity and the Gonadosomatic Index (GSI) in the Pygmy Halfbeak, D. colletei (Meisner, 2001), an intriguing viviparous fish indigenous to Sarawak, Borneo. We hypothesize that the Pygmy halfbeak, D. colletei, may exhibit unique reproductive adaptations to offset its low fecundity, thus enhancing its survival and fitness. To address this, we conducted a comprehensive study utilizing 28 mature female specimens of D. colletei, carefully measuring fecundity and GSI to shed light on the reproductive adaptations of this species. Our findings reveal that D. colletei indeed exhibits low fecundity, with a mean of 16.76 ± 2.01, and a mean GSI of 12.83 ± 1.27, providing crucial insights into the reproductive mechanisms at play in this species. These results underscore the existence of unique reproductive strategies in D. colletei, enabling its adaptation and persistence in Borneo's diverse aquatic ecosystems, and call for further ecological research to elucidate these mechanisms. This study lends to a better understanding of viviparous fish in Borneo and contributes to the broader field of aquatic ecology, enhancing our knowledge of species adaptations to unique ecological challenges.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
3. DEFINITIONS OF ML
Machine learning is the subfield of computer science that gives
computers the ability to learn without being explicitly programmed
Arthur Samuel, 1959
A computer program is said to learn from experience 𝐸 with respect to
some class of tasks 𝑇 and performance measure 𝑃 if its performance at
tasks in 𝑇, as measured by 𝑃, improves with experience 𝐸
Tom Mitchell, 1998
3
4. Supervised Learning
Input variables 𝒙
Output variable 𝑦
Mapping function 𝑦 = 𝑓(𝒙)
Unsupervised Learning
Input variables 𝒙
Learn more about the data
Reinforcement Learning
Agent acting in an environment so
as to maximize cumulative reward
4
MAIN TASKS
http://www.isaziconsulting.co.za/machinelearning.html
5. Association Rules
Items X => Items Z
Anomaly Detection
Identify unusual data points
Recommender Systems
Predict the rating that a user
would give to an item
…
5
OTHER TASKS
6. ALGORITHMS / APPROACHES / TRIBES
Discriminative vs Generative
𝑝(𝑦|𝑥) vs 𝑝(𝑦, 𝑥)
Lazy vs Eager
No learning until a test instance arrives
Parametric vs Non-Parametric
Representations (don’t) grow with
more training data
The 5 Tribes of ML
6
7. SL: LINEAR MODELS, SVMS, TREES AND NNS
7
Pedro Domingos. 2012. A few useful things to
know about machine learning. Commun. ACM 55
“MORE DATA BEATS A CLEVERER ALGORITHM”
The Economist. Facebook post, May 5th, 2017
“Those who gather the most data will
dominate the digital landscapes of the future”
8. SL: LINEAR MODELS, SVMS, TREES AND NNS
8
Pedro Domingos. 2012. A few useful things to
know about machine learning. Commun. ACM 55
“LEARN MANY MODELS, NOT JUST ONE”
Anthony Goldbloom. Kaggle CEO. Oct 2015.
“As long as Kaggle has been around, it
has almost always been ensembles of
decision trees that have won
competitions. It used to be random forest
that was the big winner, but over the last
six months a new algorithm called
XGboost has cropped up, and it’s winning
practically every competition in the
structured data category.”
17. WHAT HAS CHANGED?
Faster distributed systems
The explosion in computing power has
allowed us to use machine learning to
tackle evermore-complex problems
Exponential data growth
The explosion of data being
captured and stored has allowed us
to apply machine learning to an
ever-expanding range of domains
17
The amount of collected data is
doubling every 12 months and
will reach 44 zettabytes by 2020
19. NATURAL GAS LOAD FORECASTING
Collaboration with Gas Supply Company of
Thessaloniki & Thessaly
The problem
Daily statements of one day ahead demand must be
submitted to the regulatory entity
Actual consumption must lie within a percentage of the
statement (e.g. 10%), otherwise economic fines are imposed
Similar framework in the electricity domain
21. UNDERSTANDING ACADEMIC PUBLICATIONS
Collaboration with Atypon Inc.
Online content hosting and management software
Atypon is home to more than one-third of the world’s English-language
professional and scholarly journals — clients include Elsevier, IEEE, MIT Press,
Oxford University Press, Taylor & Francis, …
Some of the things we do
Automated semantic indexing of articles and figures
Information extraction (e.g. funding information)
Question answering
23. INDUSTRY – ACADEMIA PARTNERSHIPS
Industry funded research & development
Staff, senior researchers, and PhD students
Pro bono exploratory work
MSc theses
National and EU funding
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