A presentation on ethics in Machine Learning and Recommendation Systems given at the NYC Data Science Meetup at MeetupHQ on 12/10 http://www.meetup.com/NYC-Data-Science/events/226998694/
The document provides examples but no context or details about what those examples are examples of. It contains a single word - "Examples" - with no other text, context, or information provided.
Meer informatie over het community canvas: http://mathijsvanmeerkerk.nl/communitycanvas.html
Dit document is onderdeel van de scriptie โbeloning binnen communitiesโ: http://www.slideshare.net/Mathijsje/scriptie-5055747
Open data hackaton Rotterdam presentatie voor internet of things day. Bodyguard een Pebble en Android app je bassis van open data(straatverlichting, begroeiing en veiligheidsindex) de veiligste route naar huis bepaalt.
Using Java & Genetic Algorithms to Beat the MarketMatthew Ring
ย
The document discusses using genetic algorithms and Java to develop trading algorithms that can find repeating patterns in market data and time the market. It describes collecting market data, generating trading signals from the data, defining possible trading decisions, using genetic algorithms to evolve candidate trading strategies by simulating trades and scoring strategies based on profits, and testing the best evolved strategies on out-of-sample market data. The results showed average profits of 3% per trade for the generated trading models.
A presentation on ethics in Machine Learning and Recommendation Systems given at the NYC Data Science Meetup at MeetupHQ on 12/10 http://www.meetup.com/NYC-Data-Science/events/226998694/
The document provides examples but no context or details about what those examples are examples of. It contains a single word - "Examples" - with no other text, context, or information provided.
Meer informatie over het community canvas: http://mathijsvanmeerkerk.nl/communitycanvas.html
Dit document is onderdeel van de scriptie โbeloning binnen communitiesโ: http://www.slideshare.net/Mathijsje/scriptie-5055747
Open data hackaton Rotterdam presentatie voor internet of things day. Bodyguard een Pebble en Android app je bassis van open data(straatverlichting, begroeiing en veiligheidsindex) de veiligste route naar huis bepaalt.
Using Java & Genetic Algorithms to Beat the MarketMatthew Ring
ย
The document discusses using genetic algorithms and Java to develop trading algorithms that can find repeating patterns in market data and time the market. It describes collecting market data, generating trading signals from the data, defining possible trading decisions, using genetic algorithms to evolve candidate trading strategies by simulating trades and scoring strategies based on profits, and testing the best evolved strategies on out-of-sample market data. The results showed average profits of 3% per trade for the generated trading models.
The GENETIC ALGORITHM is a model of machine learning which derives its behavior from a metaphor of the processes of EVOLUTION in nature. Genetic Algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.
This document discusses next generation DNA sequencing technologies. It begins by describing some of the limitations of traditional Sanger sequencing, such as read lengths of 500-1000 bases and throughput of 57,000 bases per run. It then introduces some key next generation sequencing technologies, such as 454 sequencing which uses emulsion PCR and pyrosequencing to achieve read lengths of 20-100 bases but higher throughput of 20-100 Mb per run. Illumina/Solexa sequencing is also discussed, which uses sequencing by synthesis with reversible terminators and laser-based detection. Finally, third generation sequencing technologies are mentioned, such as Pacific Biosciences' single molecule real time sequencing and nanopore sequencing. In summary, the document provides a high-level
This document discusses machine intelligence and machine learning. It covers topics such as behavior-based AI vs knowledge-based AI, supervised vs unsupervised learning, classification vs prediction, and decision tree induction for classification. Decision trees are built using an algorithm that selects the attribute that best splits the data at each step to create partitions. Pruning techniques are used to avoid overfitting.
This document discusses various bio-inspired algorithms including evolutionary algorithms, swarm algorithms, immune algorithms, cultural algorithms, neural algorithms, and provides examples of their applications. It summarizes genetic algorithms and differential evolution algorithms. It also lists some popular libraries for implementing these algorithms in Python and R and provides examples.
Genetic algorithms are optimization techniques inspired by Darwin's theory of evolution. They use operations like selection, crossover and mutation to evolve solutions to problems by iteratively trying random variations. The document outlines the history, concepts, process and applications of genetic algorithms, including using them to optimize engineering design, routing, computer games and more. It describes how genetic algorithms encode potential solutions and use fitness functions to guide the evolution toward better outcomes.
Genetic algorithms are a general purpose learning algorithm that mimic the theory of evolution and natural selection. They can be used to solve complex problems by evolving solutions over multiple generations using techniques inspired by natural selection, including selection, crossover and mutation. Genetic algorithms represent potential solutions as chromosomes and evaluate them using a fitness function to determine how well they solve the problem. New solutions are created by selecting the fittest parents and breeding them using crossover and mutation operators until an optimal solution is found or a stopping criteria is reached.
Data Science - Part XIV - Genetic AlgorithmsDerek Kane
ย
This lecture provides an overview on biological evolution and genetic algorithms in a machine learning context. We will start off by going through a broad overview of the biological evolutionary process and then explore how genetic algorithms can be developed that mimic these processes. We will dive into the types of problems that can be solved with genetic algorithms and then we will conclude with a series of practical examples in R which highlights the techniques: The Knapsack Problem, Feature Selection and OLS regression, and constrained optimizations.
This document provides an overview of machine learning methods, including supervised and unsupervised learning. It discusses commonly used machine learning algorithms like support vector machines (SVM), hidden Markov models, decision trees, random forests, Bayesian networks, and neural networks. It also covers datasets, assessment metrics, and caveats to consider when using machine learning.
This document introduces machine learning algorithms. It discusses supervised and unsupervised learning problems and strategies. It provides examples of machine learning applications including neural networks for handwritten digit recognition, evolutionary algorithms for nozzle design, and Bayesian networks for gene expression analysis.
This document introduces machine learning algorithms. It discusses supervised and unsupervised learning problems and strategies. It provides examples of machine learning applications including neural networks for handwritten digit recognition, evolutionary algorithms for nozzle design, and Bayesian networks for gene expression analysis.
The document discusses genetic algorithms, which are inspired by biological evolution. It explains that genetic algorithms use populations of candidate solutions that undergo processes of selection, crossover and mutation to evolve toward better solutions. It provides examples of representing solutions as binary strings and calculating their fitness to problems. The basic genetic algorithm is outlined as generating random populations, evaluating fitness, selecting parents for recombination, applying crossover and mutation, and iterating toward improving solutions over generations.
Towards Prediction of Platinum Treatment Response in Ovarian Cancer using Mac...Antoaneta Vladimirova
ย
1) Machine learning models were used to predict platinum treatment response in ovarian cancer using gene expression data from The Cancer Genome Atlas.
2) Several machine learning algorithms including logistic regression, random forests, support vector machines, and artificial neural networks were able to predict platinum resistance versus sensitivity with around 80% accuracy.
3) Artificial neural networks performed the best, likely due to their more complex model structure, while prediction using only clinical data achieved around 75% accuracy.
The document discusses genetic algorithms and their key components. It defines genetic algorithms as search techniques that use principles of evolution and natural genetics to optimize problem solutions. Genetic algorithms work with a population of potential solutions that evolves toward better solutions, selecting individuals with higher fitness and breeding them through crossover and mutation operators to produce new generations. The document outlines the genetic representation of solutions, fitness functions to evaluate solutions, and the basic process of genetic algorithms through generations of selection, crossover and mutation.
Useful Techniques in Artificial IntelligenceIla Group
ย
The document discusses artificial intelligence techniques presented by Dr. Will Browne at Cranfield University. It provides examples of applications of AI techniques in various fields such as finance, industry, engineering and control. It then describes common AI techniques such as expert systems, case-based reasoning, genetic algorithms, neural networks, fuzzy logic and cellular automata. The document emphasizes exploring appropriate techniques for problems and avoiding issues like lack of transparency, garbage in-garbage out, and difficulties generalizing from training data.
Genetic algorithms are search algorithms inspired by biological evolution that use techniques like mutation, crossover, and selection to evolve solutions to problems. They represent potential solutions as individuals in a population and evolve the population over multiple generations using genetic operators to improve the overall quality of solutions. Genetic programming is a type of genetic algorithm that evolves computer programs to solve problems by genetically breeding populations of computer programs.
This document provides an overview of the Lab for Bioinformatics and Computational Genomics at a university. It describes that the lab has over 100 people from diverse backgrounds including engineers, scientists, technicians, geneticists and clinicians. The lab's work involves hardware/software engineering, mathematics, molecular biology and analysis of biological data through computing. Bioinformatics is defined as the application of information technology to biological data, including tasks like sequence analysis, molecular modeling, phylogeny analysis, medical applications and more. The document then discusses some of the promises and applications of genomics and bioinformatics in fields like medicine, agriculture and animal health.
This document describes genetic algorithms and provides an example of how one works. It defines genetic algorithms as evolutionary algorithms that use techniques inspired by evolutionary biology like inheritance, mutation, selection, and crossover. The document then outlines the typical components of a genetic algorithm, including initialization of a random population, fitness evaluation, selection of parents, crossover and mutation to produce offspring, and iteration until a termination condition is met. It concludes by showing pseudocode for a genetic algorithm to solve the onemax problem and output from running the algorithm.
This document provides an overview of a machine learning course. It outlines the course structure, including topics covered, assignments, and grading. The course covers fundamental machine learning algorithms for classification, regression, clustering, and dimensionality reduction. It also discusses applications of machine learning like spam filtering, recommender systems, and chess playing computers.
This document provides an overview of a machine learning course. It outlines the course structure, including topics covered, assignments, and grading. The course covers fundamental machine learning algorithms for classification, regression, clustering, and dimensionality reduction. It also discusses applications of machine learning like spam filtering, recommender systems, and chess playing programs.
This letter congratulates the recipient for scoring above 90% in the online, non-credit course "Model Thinking" taught by Professor Scott E. Page of the University of Michigan-Ann Arbor. The course was authorized by the University of Michigan-Ann Arbor. The recipient's name and email address are provided.
The GENETIC ALGORITHM is a model of machine learning which derives its behavior from a metaphor of the processes of EVOLUTION in nature. Genetic Algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.
This document discusses next generation DNA sequencing technologies. It begins by describing some of the limitations of traditional Sanger sequencing, such as read lengths of 500-1000 bases and throughput of 57,000 bases per run. It then introduces some key next generation sequencing technologies, such as 454 sequencing which uses emulsion PCR and pyrosequencing to achieve read lengths of 20-100 bases but higher throughput of 20-100 Mb per run. Illumina/Solexa sequencing is also discussed, which uses sequencing by synthesis with reversible terminators and laser-based detection. Finally, third generation sequencing technologies are mentioned, such as Pacific Biosciences' single molecule real time sequencing and nanopore sequencing. In summary, the document provides a high-level
This document discusses machine intelligence and machine learning. It covers topics such as behavior-based AI vs knowledge-based AI, supervised vs unsupervised learning, classification vs prediction, and decision tree induction for classification. Decision trees are built using an algorithm that selects the attribute that best splits the data at each step to create partitions. Pruning techniques are used to avoid overfitting.
This document discusses various bio-inspired algorithms including evolutionary algorithms, swarm algorithms, immune algorithms, cultural algorithms, neural algorithms, and provides examples of their applications. It summarizes genetic algorithms and differential evolution algorithms. It also lists some popular libraries for implementing these algorithms in Python and R and provides examples.
Genetic algorithms are optimization techniques inspired by Darwin's theory of evolution. They use operations like selection, crossover and mutation to evolve solutions to problems by iteratively trying random variations. The document outlines the history, concepts, process and applications of genetic algorithms, including using them to optimize engineering design, routing, computer games and more. It describes how genetic algorithms encode potential solutions and use fitness functions to guide the evolution toward better outcomes.
Genetic algorithms are a general purpose learning algorithm that mimic the theory of evolution and natural selection. They can be used to solve complex problems by evolving solutions over multiple generations using techniques inspired by natural selection, including selection, crossover and mutation. Genetic algorithms represent potential solutions as chromosomes and evaluate them using a fitness function to determine how well they solve the problem. New solutions are created by selecting the fittest parents and breeding them using crossover and mutation operators until an optimal solution is found or a stopping criteria is reached.
Data Science - Part XIV - Genetic AlgorithmsDerek Kane
ย
This lecture provides an overview on biological evolution and genetic algorithms in a machine learning context. We will start off by going through a broad overview of the biological evolutionary process and then explore how genetic algorithms can be developed that mimic these processes. We will dive into the types of problems that can be solved with genetic algorithms and then we will conclude with a series of practical examples in R which highlights the techniques: The Knapsack Problem, Feature Selection and OLS regression, and constrained optimizations.
This document provides an overview of machine learning methods, including supervised and unsupervised learning. It discusses commonly used machine learning algorithms like support vector machines (SVM), hidden Markov models, decision trees, random forests, Bayesian networks, and neural networks. It also covers datasets, assessment metrics, and caveats to consider when using machine learning.
This document introduces machine learning algorithms. It discusses supervised and unsupervised learning problems and strategies. It provides examples of machine learning applications including neural networks for handwritten digit recognition, evolutionary algorithms for nozzle design, and Bayesian networks for gene expression analysis.
This document introduces machine learning algorithms. It discusses supervised and unsupervised learning problems and strategies. It provides examples of machine learning applications including neural networks for handwritten digit recognition, evolutionary algorithms for nozzle design, and Bayesian networks for gene expression analysis.
The document discusses genetic algorithms, which are inspired by biological evolution. It explains that genetic algorithms use populations of candidate solutions that undergo processes of selection, crossover and mutation to evolve toward better solutions. It provides examples of representing solutions as binary strings and calculating their fitness to problems. The basic genetic algorithm is outlined as generating random populations, evaluating fitness, selecting parents for recombination, applying crossover and mutation, and iterating toward improving solutions over generations.
Towards Prediction of Platinum Treatment Response in Ovarian Cancer using Mac...Antoaneta Vladimirova
ย
1) Machine learning models were used to predict platinum treatment response in ovarian cancer using gene expression data from The Cancer Genome Atlas.
2) Several machine learning algorithms including logistic regression, random forests, support vector machines, and artificial neural networks were able to predict platinum resistance versus sensitivity with around 80% accuracy.
3) Artificial neural networks performed the best, likely due to their more complex model structure, while prediction using only clinical data achieved around 75% accuracy.
The document discusses genetic algorithms and their key components. It defines genetic algorithms as search techniques that use principles of evolution and natural genetics to optimize problem solutions. Genetic algorithms work with a population of potential solutions that evolves toward better solutions, selecting individuals with higher fitness and breeding them through crossover and mutation operators to produce new generations. The document outlines the genetic representation of solutions, fitness functions to evaluate solutions, and the basic process of genetic algorithms through generations of selection, crossover and mutation.
Useful Techniques in Artificial IntelligenceIla Group
ย
The document discusses artificial intelligence techniques presented by Dr. Will Browne at Cranfield University. It provides examples of applications of AI techniques in various fields such as finance, industry, engineering and control. It then describes common AI techniques such as expert systems, case-based reasoning, genetic algorithms, neural networks, fuzzy logic and cellular automata. The document emphasizes exploring appropriate techniques for problems and avoiding issues like lack of transparency, garbage in-garbage out, and difficulties generalizing from training data.
Genetic algorithms are search algorithms inspired by biological evolution that use techniques like mutation, crossover, and selection to evolve solutions to problems. They represent potential solutions as individuals in a population and evolve the population over multiple generations using genetic operators to improve the overall quality of solutions. Genetic programming is a type of genetic algorithm that evolves computer programs to solve problems by genetically breeding populations of computer programs.
This document provides an overview of the Lab for Bioinformatics and Computational Genomics at a university. It describes that the lab has over 100 people from diverse backgrounds including engineers, scientists, technicians, geneticists and clinicians. The lab's work involves hardware/software engineering, mathematics, molecular biology and analysis of biological data through computing. Bioinformatics is defined as the application of information technology to biological data, including tasks like sequence analysis, molecular modeling, phylogeny analysis, medical applications and more. The document then discusses some of the promises and applications of genomics and bioinformatics in fields like medicine, agriculture and animal health.
This document describes genetic algorithms and provides an example of how one works. It defines genetic algorithms as evolutionary algorithms that use techniques inspired by evolutionary biology like inheritance, mutation, selection, and crossover. The document then outlines the typical components of a genetic algorithm, including initialization of a random population, fitness evaluation, selection of parents, crossover and mutation to produce offspring, and iteration until a termination condition is met. It concludes by showing pseudocode for a genetic algorithm to solve the onemax problem and output from running the algorithm.
This document provides an overview of a machine learning course. It outlines the course structure, including topics covered, assignments, and grading. The course covers fundamental machine learning algorithms for classification, regression, clustering, and dimensionality reduction. It also discusses applications of machine learning like spam filtering, recommender systems, and chess playing computers.
This document provides an overview of a machine learning course. It outlines the course structure, including topics covered, assignments, and grading. The course covers fundamental machine learning algorithms for classification, regression, clustering, and dimensionality reduction. It also discusses applications of machine learning like spam filtering, recommender systems, and chess playing programs.
This letter congratulates the recipient for scoring above 90% in the online, non-credit course "Model Thinking" taught by Professor Scott E. Page of the University of Michigan-Ann Arbor. The course was authorized by the University of Michigan-Ann Arbor. The recipient's name and email address are provided.
The document outlines the design for an augmented reality game app including various events, designs, client and server interactions. It describes:
1) The main screen event which would include designing map markers, a bottom menu, and pop-up windows to display item information. The client would place markers and menus while the server syncs location data and provides pop-ups.
2) An "at goods" event where picking up an item worth 10 credits would add the value to the user's account from the server.
3) A "place trap" event where the client could set a trap by sending data to the server, which would then place it and deduct 30 credits from the user's balance.
This document discusses designing everyday objects to be meaningful, touchable, lovable, and desirable through moving and changing designs that people can touch and feel as part of a regularly changing collection according to Karim's design alphabet while keeping products affordable.
The document discusses Zorg 2029 and RepRap. It mentions PressureCooker and RepRap multiple times. The document seems focused on 3D printing technology and open source hardware projects.
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
ย
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
ย
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
ย
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
ย
(๐๐๐ ๐๐๐) (๐๐๐ฌ๐ฌ๐จ๐ง ๐)-๐๐ซ๐๐ฅ๐ข๐ฆ๐ฌ
๐๐ข๐ฌ๐๐ฎ๐ฌ๐ฌ ๐ญ๐ก๐ ๐๐๐ ๐๐ฎ๐ซ๐ซ๐ข๐๐ฎ๐ฅ๐ฎ๐ฆ ๐ข๐ง ๐ญ๐ก๐ ๐๐ก๐ข๐ฅ๐ข๐ฉ๐ฉ๐ข๐ง๐๐ฌ:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
๐๐ฑ๐ฉ๐ฅ๐๐ข๐ง ๐ญ๐ก๐ ๐๐๐ญ๐ฎ๐ซ๐ ๐๐ง๐ ๐๐๐จ๐ฉ๐ ๐จ๐ ๐๐ง ๐๐ง๐ญ๐ซ๐๐ฉ๐ซ๐๐ง๐๐ฎ๐ซ:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
ย
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
CapTechTalks Webinar Slides June 2024 Donovan Wright.pptxCapitolTechU
ย
Slides from a Capitol Technology University webinar held June 20, 2024. The webinar featured Dr. Donovan Wright, presenting on the Department of Defense Digital Transformation.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the bodyโs response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.