The document discusses machine learning and genetic algorithms. It provides definitions of machine learning as the study of processes that lead to self-improvement of machine performance through experience. It also discusses different types of learning including supervised learning, unsupervised learning, and reinforcement learning. The document then explains genetic algorithms as evolutionary algorithms that use operations like mutation and crossover to evolve solutions to problems over multiple generations.
This document provides an introduction to machine learning and inductive inference. It discusses what machine learning is, common learning tasks like concept learning and function learning, different data representations, and example applications such as knowledge discovery and building adaptive systems. The course will cover generalizing from specific examples to broader concepts through inductive inference and different learning approaches.
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 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.
Three main types of machine learning are supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a model using labeled input/output data where the desired outputs are provided, allowing the model to map inputs to outputs. Unsupervised learning involves discovering hidden patterns in unlabeled data and grouping similar data points together. Reinforcement learning involves an agent learning through trial-and-error interactions with a dynamic environment by receiving rewards or punishments for actions.
The document discusses reinforcement learning, including Q-learning. It provides an overview of reinforcement learning, describing what it is, important machine learning algorithms for it like Q-learning, and how Q-learning works in theory and practice. It also discusses challenges of reinforcement learning, potential applications, and links between reinforcement learning algorithms and human psychology.
Computational Biology, Part 4 Protein Coding Regionsbutest
The document discusses different machine learning approaches for supervised classification and sequence analysis. It describes several classification algorithms like k-nearest neighbors, decision trees, linear discriminants, and support vector machines. It also discusses evaluating classifiers using cross-validation and confusion matrices. For sequence analysis, it covers using position-specific scoring matrices, hidden Markov models, cobbling, and family pairwise search to identify new members of protein families. It compares the performance of these different machine learning methods on sequence analysis tasks.
The document provides an overview of machine learning algorithms and concepts, including:
- Supervised learning algorithms like regression and classification that use labeled training data to predict target values or categories. Unsupervised learning algorithms like clustering that find hidden patterns in unlabeled data.
- Popular Python libraries for machine learning like NumPy, SciPy, Matplotlib, and Scikit-learn that make implementing algorithms more convenient.
- Examples of supervised and unsupervised learning using a toy that teaches a child to sort shapes or find patterns without explicit labeling of data.
- Definitions of artificial intelligence, machine learning, and deep learning, and how they relate to each other.
This document provides an introduction to machine learning and inductive inference. It discusses what machine learning is, common learning tasks like concept learning and function learning, different data representations, and example applications such as knowledge discovery and building adaptive systems. The course will cover generalizing from specific examples to broader concepts through inductive inference and different learning approaches.
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 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.
Three main types of machine learning are supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a model using labeled input/output data where the desired outputs are provided, allowing the model to map inputs to outputs. Unsupervised learning involves discovering hidden patterns in unlabeled data and grouping similar data points together. Reinforcement learning involves an agent learning through trial-and-error interactions with a dynamic environment by receiving rewards or punishments for actions.
The document discusses reinforcement learning, including Q-learning. It provides an overview of reinforcement learning, describing what it is, important machine learning algorithms for it like Q-learning, and how Q-learning works in theory and practice. It also discusses challenges of reinforcement learning, potential applications, and links between reinforcement learning algorithms and human psychology.
Computational Biology, Part 4 Protein Coding Regionsbutest
The document discusses different machine learning approaches for supervised classification and sequence analysis. It describes several classification algorithms like k-nearest neighbors, decision trees, linear discriminants, and support vector machines. It also discusses evaluating classifiers using cross-validation and confusion matrices. For sequence analysis, it covers using position-specific scoring matrices, hidden Markov models, cobbling, and family pairwise search to identify new members of protein families. It compares the performance of these different machine learning methods on sequence analysis tasks.
The document provides an overview of machine learning algorithms and concepts, including:
- Supervised learning algorithms like regression and classification that use labeled training data to predict target values or categories. Unsupervised learning algorithms like clustering that find hidden patterns in unlabeled data.
- Popular Python libraries for machine learning like NumPy, SciPy, Matplotlib, and Scikit-learn that make implementing algorithms more convenient.
- Examples of supervised and unsupervised learning using a toy that teaches a child to sort shapes or find patterns without explicit labeling of data.
- Definitions of artificial intelligence, machine learning, and deep learning, and how they relate to each other.
1. The document discusses machine learning and provides an overview of key concepts like inductive reasoning, learning from examples, and the constituents of machine learning problems.
2. It explains that machine learning problems involve an example set, background concepts, background axioms, and potential errors in data. Common machine learning tasks are categorization and prediction.
3. The document also outlines the constituents of machine learning methods, including representation schemes, search methods, and approaches for selecting hypotheses when multiple solutions are produced.
1. The document discusses machine learning and provides an overview of key concepts like inductive reasoning, learning from examples, and the constituents of machine learning problems.
2. It explains that machine learning problems involve an example set, background concepts, background axioms, and potential errors in data. Common machine learning tasks are categorization and prediction.
3. The document also outlines the constituents of machine learning methods, including representation schemes, search methods, and approaches for selecting hypotheses when multiple solutions are produced.
Short Description about machine learning.What is machine learning? specifications , categories, terminologies and applications every thing is explained in short way.
This lecture covers machine learning concepts including definitions, applications, learning agents, different types of learning (supervised, unsupervised, reinforcement), terms like training set and test set, decision tree learning using information gain to select attributes, and Bayesian learning including Bayes' theorem and naive Bayesian classification of documents. Key applications discussed include spam filtering, autonomous vehicles, medical data mining, and predicting patient risk.
This lecture covers machine learning concepts including definitions, applications, learning agents, different types of learning (supervised, unsupervised, reinforcement), terms like training set and test set, decision tree learning using information gain to select attributes, and Bayesian learning including Bayes' theorem and naive Bayesian classification of documents. Key applications discussed include spam filtering, autonomous driving, and medical data mining.
Deep Reinforcement Learning Through Policy Optimization, John Schulman, OpenAIJack Clark
This document discusses deep reinforcement learning through policy optimization. It begins with an introduction to reinforcement learning and how deep neural networks can be used to approximate policies, value functions, and models. It then discusses how deep reinforcement learning can be applied to problems in robotics, business operations, and other machine learning domains. The document reviews how reinforcement learning relates to other machine learning problems like supervised learning and contextual bandits. It provides an overview of policy gradient methods and the cross-entropy method for policy optimization before discussing Markov decision processes, parameterized policies, and specific policy gradient algorithms like the vanilla policy gradient algorithm and trust region policy optimization.
The document discusses machine learning techniques. It describes how machines can learn from examples, through experience and adaptation. It evaluates methods for acquiring and representing knowledge, including decision trees, neural networks and genetic algorithms. While machine learning techniques have benefits like learning from experience and generalizing, they also have drawbacks such as not knowing whether the learned knowledge is completely correct.
The document discusses machine learning and provides information about several key concepts:
1) Machine learning allows computer systems to learn from data without being explicitly programmed by using statistical techniques to identify patterns in large amounts of data.
2) There are three main approaches to machine learning: supervised learning which uses labeled data to build predictive models, unsupervised learning which finds patterns in unlabeled data, and reinforcement learning which learns from success and failures.
3) Effective machine learning requires balancing model complexity, amount of training data, and ability to generalize to new examples in order to avoid underfitting or overfitting the data. Learning algorithms aim to minimize these risks.
This document provides an introduction and overview of machine learning algorithms. It begins by discussing the importance and growth of machine learning. It then describes the three main types of machine learning algorithms: supervised learning, unsupervised learning, and reinforcement learning. Next, it lists and briefly defines ten commonly used machine learning algorithms including linear regression, logistic regression, decision trees, SVM, Naive Bayes, and KNN. For each algorithm, it provides a simplified example to illustrate how it works along with sample Python and R code.
GradTrack: Getting Started with Statistics September 20, 2018Nancy Garmer
Dr. Gary Burns, Professor, School of Psychology, Florida Institute of Technology Evans Library Introduction to Statistics: Don't be afraid
Video presentation with audio available on YouTube:http://bit.ly/GradTrackStatistics2018
YouTube Presentation: http://bit.ly/GradTrackStatistics2018
Dr. Gary Burns, Professor, School of Psychology, Florida Institute of Technology, Evans Library GradTrack Workshop
This document discusses machine learning concepts of concept learning and decision-tree learning. It describes concept learning as inferring a boolean function from training examples and using algorithms like Candidate Elimination to search the hypothesis space. Decision tree learning is explained as representing classification functions as trees with nodes testing attributes, allowing disjunctive concepts. The ID3 algorithm is presented as a greedy top-down search that selects the best attribute at each node using information gain, potentially overfitting data without pruning or a validation set.
In this lecture, I will present a general tour of some of the most commonly used kernel methods in statistical machine learning and data mining. I will touch on elements of artificial neural networks and then highlight their intricate connections to some general purpose kernel methods like Gaussian process learning machines. I will also resurrect the famous universal approximation theorem and will most likely ignite a [controversial] debate around the theme: could it be that [shallow] networks like radial basis function networks or Gaussian processes are all we need for well-behaved functions? Do we really need many hidden layers as the hype around Deep Neural Network architectures seem to suggest or should we heed Ockham’s principle of parsimony, namely “Entities should not be multiplied beyond necessity.” (“Entia non sunt multiplicanda praeter necessitatem.”) I intend to spend the last 15 minutes of this lecture sharing my personal tips and suggestions with our precious postdoctoral fellows on how to make the most of their experience.
This document provides an overview of machine learning concepts and techniques including linear regression, logistic regression, unsupervised learning, and k-means clustering. It discusses how machine learning involves using data to train models that can then be used to make predictions on new data. Key machine learning types covered are supervised learning (regression, classification), unsupervised learning (clustering), and reinforcement learning. Example machine learning applications are also mentioned such as spam filtering, recommender systems, and autonomous vehicles.
This document provides an overview of descriptive statistics, inferential statistics, and regression analysis using PASW Statistics software. It discusses topics such as frequency analysis, measures of central tendency, hypothesis testing, t-tests, ANOVA, chi-square tests, correlation, and linear regression. The document is divided into multiple parts that cover opening and manipulating data files, descriptive statistics, tests of significance, regression analysis, and chi-square/ANOVA. It also discusses importing/exporting data and using scripts in PASW Statistics.
Machine learning involves developing systems that can learn from data and experience. The document discusses several machine learning techniques including decision tree learning, rule induction, case-based reasoning, supervised and unsupervised learning. It also covers representations, learners, critics and applications of machine learning such as improving search engines and developing intelligent tutoring systems.
This document provides an overview of machine learning concepts including supervised learning, unsupervised learning, and reinforcement learning. It discusses common machine learning applications and challenges. Key topics covered include linear regression, classification, clustering, neural networks, bias-variance tradeoff, and model selection. Evaluation techniques like training error, validation error, and test error are also summarized.
Sean Holden (University of Cambridge) - Proving Theorems_ Still A Major Test ...Codiax
This document discusses applying machine learning techniques to automated theorem proving and formal proof checking. It begins by providing background on logic-based theorem proving and efforts to formally prove mathematical theorems. It then discusses using machine learning to help guide automated theorem provers by selecting optimal heuristics and recommending useful lemmas. The document concludes by noting the challenges of developing mathematical languages that are both natural for humans and amenable to formal verification.
Machine learning involves using algorithms to learn from data and make predictions. There are different types of machine learning problems including supervised learning (classification and regression), unsupervised learning (clustering and dimensionality reduction), and reinforcement learning. Supervised learning involves predicting outcomes based on labeled training data, while unsupervised learning finds patterns in unlabeled data. The document provides definitions and examples to explain machine learning concepts such as learning types, variables, linear separability, and the generalization process.
This document provides an overview of machine learning applications in natural language processing and text classification. It discusses common machine learning tasks like part-of-speech tagging, named entity extraction, and text classification. Popular machine learning algorithms for classification are described, including k-nearest neighbors, Rocchio classification, support vector machines, bagging, and boosting. The document argues that machine learning can be used to solve complex real-world problems and that text processing is one area with many potential applications of these techniques.
The document discusses the Clinical Natural Language Processing (cNLP) project. It summarizes the following key points:
1) The cNLP project involves multiple investigators from various institutions with the aim of extracting structured information from unstructured clinical text using natural language processing techniques.
2) In year 1, the project focused on developing gold standard clinical corpora for evaluation and annotation guidelines. Software deliverables included modules for dependency parsing, drug extraction, and smoking status classification.
3) Going forward, the project will focus on relationship extraction, comparative effectiveness research using extracted data, and integrating the resulting natural language processing tool (cTAKES) with other clinical systems and databases.
Using Artificial Intelligence in the field of Diagnostics_Case Studies.pptRohanBorgalli
This document discusses using artificial intelligence in diagnostics. It notes that 2.5 billion gigabytes of data are produced daily, and 80% of the world's population lives in low-income communities. AI shows potential to improve diagnostics by analyzing large amounts of data and enhancing accessibility and accuracy of diagnoses. Examples discussed include using AI to detect diabetic retinopathy from eye scans and to assess risk of major depressive disorder from social media posts. Overall, the document examines how AI models can be developed and improved to better serve global healthcare needs.
1. The document discusses machine learning and provides an overview of key concepts like inductive reasoning, learning from examples, and the constituents of machine learning problems.
2. It explains that machine learning problems involve an example set, background concepts, background axioms, and potential errors in data. Common machine learning tasks are categorization and prediction.
3. The document also outlines the constituents of machine learning methods, including representation schemes, search methods, and approaches for selecting hypotheses when multiple solutions are produced.
1. The document discusses machine learning and provides an overview of key concepts like inductive reasoning, learning from examples, and the constituents of machine learning problems.
2. It explains that machine learning problems involve an example set, background concepts, background axioms, and potential errors in data. Common machine learning tasks are categorization and prediction.
3. The document also outlines the constituents of machine learning methods, including representation schemes, search methods, and approaches for selecting hypotheses when multiple solutions are produced.
Short Description about machine learning.What is machine learning? specifications , categories, terminologies and applications every thing is explained in short way.
This lecture covers machine learning concepts including definitions, applications, learning agents, different types of learning (supervised, unsupervised, reinforcement), terms like training set and test set, decision tree learning using information gain to select attributes, and Bayesian learning including Bayes' theorem and naive Bayesian classification of documents. Key applications discussed include spam filtering, autonomous vehicles, medical data mining, and predicting patient risk.
This lecture covers machine learning concepts including definitions, applications, learning agents, different types of learning (supervised, unsupervised, reinforcement), terms like training set and test set, decision tree learning using information gain to select attributes, and Bayesian learning including Bayes' theorem and naive Bayesian classification of documents. Key applications discussed include spam filtering, autonomous driving, and medical data mining.
Deep Reinforcement Learning Through Policy Optimization, John Schulman, OpenAIJack Clark
This document discusses deep reinforcement learning through policy optimization. It begins with an introduction to reinforcement learning and how deep neural networks can be used to approximate policies, value functions, and models. It then discusses how deep reinforcement learning can be applied to problems in robotics, business operations, and other machine learning domains. The document reviews how reinforcement learning relates to other machine learning problems like supervised learning and contextual bandits. It provides an overview of policy gradient methods and the cross-entropy method for policy optimization before discussing Markov decision processes, parameterized policies, and specific policy gradient algorithms like the vanilla policy gradient algorithm and trust region policy optimization.
The document discusses machine learning techniques. It describes how machines can learn from examples, through experience and adaptation. It evaluates methods for acquiring and representing knowledge, including decision trees, neural networks and genetic algorithms. While machine learning techniques have benefits like learning from experience and generalizing, they also have drawbacks such as not knowing whether the learned knowledge is completely correct.
The document discusses machine learning and provides information about several key concepts:
1) Machine learning allows computer systems to learn from data without being explicitly programmed by using statistical techniques to identify patterns in large amounts of data.
2) There are three main approaches to machine learning: supervised learning which uses labeled data to build predictive models, unsupervised learning which finds patterns in unlabeled data, and reinforcement learning which learns from success and failures.
3) Effective machine learning requires balancing model complexity, amount of training data, and ability to generalize to new examples in order to avoid underfitting or overfitting the data. Learning algorithms aim to minimize these risks.
This document provides an introduction and overview of machine learning algorithms. It begins by discussing the importance and growth of machine learning. It then describes the three main types of machine learning algorithms: supervised learning, unsupervised learning, and reinforcement learning. Next, it lists and briefly defines ten commonly used machine learning algorithms including linear regression, logistic regression, decision trees, SVM, Naive Bayes, and KNN. For each algorithm, it provides a simplified example to illustrate how it works along with sample Python and R code.
GradTrack: Getting Started with Statistics September 20, 2018Nancy Garmer
Dr. Gary Burns, Professor, School of Psychology, Florida Institute of Technology Evans Library Introduction to Statistics: Don't be afraid
Video presentation with audio available on YouTube:http://bit.ly/GradTrackStatistics2018
YouTube Presentation: http://bit.ly/GradTrackStatistics2018
Dr. Gary Burns, Professor, School of Psychology, Florida Institute of Technology, Evans Library GradTrack Workshop
This document discusses machine learning concepts of concept learning and decision-tree learning. It describes concept learning as inferring a boolean function from training examples and using algorithms like Candidate Elimination to search the hypothesis space. Decision tree learning is explained as representing classification functions as trees with nodes testing attributes, allowing disjunctive concepts. The ID3 algorithm is presented as a greedy top-down search that selects the best attribute at each node using information gain, potentially overfitting data without pruning or a validation set.
In this lecture, I will present a general tour of some of the most commonly used kernel methods in statistical machine learning and data mining. I will touch on elements of artificial neural networks and then highlight their intricate connections to some general purpose kernel methods like Gaussian process learning machines. I will also resurrect the famous universal approximation theorem and will most likely ignite a [controversial] debate around the theme: could it be that [shallow] networks like radial basis function networks or Gaussian processes are all we need for well-behaved functions? Do we really need many hidden layers as the hype around Deep Neural Network architectures seem to suggest or should we heed Ockham’s principle of parsimony, namely “Entities should not be multiplied beyond necessity.” (“Entia non sunt multiplicanda praeter necessitatem.”) I intend to spend the last 15 minutes of this lecture sharing my personal tips and suggestions with our precious postdoctoral fellows on how to make the most of their experience.
This document provides an overview of machine learning concepts and techniques including linear regression, logistic regression, unsupervised learning, and k-means clustering. It discusses how machine learning involves using data to train models that can then be used to make predictions on new data. Key machine learning types covered are supervised learning (regression, classification), unsupervised learning (clustering), and reinforcement learning. Example machine learning applications are also mentioned such as spam filtering, recommender systems, and autonomous vehicles.
This document provides an overview of descriptive statistics, inferential statistics, and regression analysis using PASW Statistics software. It discusses topics such as frequency analysis, measures of central tendency, hypothesis testing, t-tests, ANOVA, chi-square tests, correlation, and linear regression. The document is divided into multiple parts that cover opening and manipulating data files, descriptive statistics, tests of significance, regression analysis, and chi-square/ANOVA. It also discusses importing/exporting data and using scripts in PASW Statistics.
Machine learning involves developing systems that can learn from data and experience. The document discusses several machine learning techniques including decision tree learning, rule induction, case-based reasoning, supervised and unsupervised learning. It also covers representations, learners, critics and applications of machine learning such as improving search engines and developing intelligent tutoring systems.
This document provides an overview of machine learning concepts including supervised learning, unsupervised learning, and reinforcement learning. It discusses common machine learning applications and challenges. Key topics covered include linear regression, classification, clustering, neural networks, bias-variance tradeoff, and model selection. Evaluation techniques like training error, validation error, and test error are also summarized.
Sean Holden (University of Cambridge) - Proving Theorems_ Still A Major Test ...Codiax
This document discusses applying machine learning techniques to automated theorem proving and formal proof checking. It begins by providing background on logic-based theorem proving and efforts to formally prove mathematical theorems. It then discusses using machine learning to help guide automated theorem provers by selecting optimal heuristics and recommending useful lemmas. The document concludes by noting the challenges of developing mathematical languages that are both natural for humans and amenable to formal verification.
Machine learning involves using algorithms to learn from data and make predictions. There are different types of machine learning problems including supervised learning (classification and regression), unsupervised learning (clustering and dimensionality reduction), and reinforcement learning. Supervised learning involves predicting outcomes based on labeled training data, while unsupervised learning finds patterns in unlabeled data. The document provides definitions and examples to explain machine learning concepts such as learning types, variables, linear separability, and the generalization process.
This document provides an overview of machine learning applications in natural language processing and text classification. It discusses common machine learning tasks like part-of-speech tagging, named entity extraction, and text classification. Popular machine learning algorithms for classification are described, including k-nearest neighbors, Rocchio classification, support vector machines, bagging, and boosting. The document argues that machine learning can be used to solve complex real-world problems and that text processing is one area with many potential applications of these techniques.
The document discusses the Clinical Natural Language Processing (cNLP) project. It summarizes the following key points:
1) The cNLP project involves multiple investigators from various institutions with the aim of extracting structured information from unstructured clinical text using natural language processing techniques.
2) In year 1, the project focused on developing gold standard clinical corpora for evaluation and annotation guidelines. Software deliverables included modules for dependency parsing, drug extraction, and smoking status classification.
3) Going forward, the project will focus on relationship extraction, comparative effectiveness research using extracted data, and integrating the resulting natural language processing tool (cTAKES) with other clinical systems and databases.
Using Artificial Intelligence in the field of Diagnostics_Case Studies.pptRohanBorgalli
This document discusses using artificial intelligence in diagnostics. It notes that 2.5 billion gigabytes of data are produced daily, and 80% of the world's population lives in low-income communities. AI shows potential to improve diagnostics by analyzing large amounts of data and enhancing accessibility and accuracy of diagnoses. Examples discussed include using AI to detect diabetic retinopathy from eye scans and to assess risk of major depressive disorder from social media posts. Overall, the document examines how AI models can be developed and improved to better serve global healthcare needs.
This document discusses key concepts of software architecture. It makes three main points:
1) Architecture is not just a phase of development but rather is fundamental to software. Every application has an architecture and architect.
2) Considering architecture throughout the development lifecycle leads to better requirements analysis, design, implementation, testing, and evolution.
3) The "Turbine Model" visualizes development activities over time to show how architecture can be central to the process.
The document discusses the envisioned development of automated vehicle technology from 2020 to 2050. It outlines that by 2020, vehicles may have limited self-driving capabilities (Level 3 automation), major automakers are developing key self-driving technologies, and concepts show interior transformations as vehicles take over driving tasks. By 2030, vehicles may have full self-driving capabilities (Level 4 automation). By 2050, nearly all vehicles are expected to have full self-driving automation and it will be a standard feature in new vehicles. The document also includes a glossary defining technologies involved in automated driving like LIDAR, vehicle-to-vehicle communication, and vehicle platooning.
The document discusses the benefits of exercise for mental health. It states that regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help alleviate symptoms of mental illness.
This document provides an overview of an introductory course on Arduino prototyping. The course covers installing the Arduino integrated development environment and libraries, electrical components like resistors and LEDs, programming basics, and virtual prototyping tools. It then discusses the Arduino board features, different input and output types, and demonstrates building a simple LED circuit on a breadboard. The document emphasizes hands-on learning and introduces concepts like analog and digital signals to help students start prototyping with Arduino.
PCA is a dimensionality reduction technique that uses linear transformations to project high-dimensional data onto a lower-dimensional space while retaining as much information as possible. It works by identifying patterns in data and expressing the data in such a way as to highlight their similarities and differences. Specifically, PCA uses linear combinations of the original variables to extract the most important patterns from the data in the form of principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible.
This document provides an overview of Rob Parker's upcoming presentation on telecommunications. The presentation will cover:
1) The history of telecommunications, from physical delivery methods to early electrical systems like the telegraph.
2) Present and future fixed (cabled) and mobile (wireless) telecommunication systems.
3) Applications of present and future telecommunications technologies.
4) Parker's personal view of where telecommunications will be in 10 years.
The presentation aims to educate attendees on the evolution of telecommunications and new technical developments, and spark debate about emerging technologies.
This document discusses factor analysis, including:
- Factor analysis is used for data reduction, scale development, and assessing dimensionality. It identifies underlying factors or dimensions from a set of interrelated variables.
- The key steps in factor analysis are computing a correlation matrix, extracting factors using methods like principal component analysis, rotating factors, and determining the optimal number of factors.
- The document provides guidance on interpreting factor analysis results and deciding how many factors best represent the data.
The document provides an introduction to time series analysis and forecasting using KNIME. It discusses key concepts in time series such as trend, seasonality, cycles and residuals. It also presents examples of time series data and applications of time series analysis. The task is to analyze electricity consumption time series data from Ireland to predict hourly consumption. Techniques like clustering, ARIMA and neural networks will be applied to generate forecasts.
The document summarizes several papers on image captioning using neural networks. It describes Karpathy and Fei-Fei (2015), which obtains image and sentence embeddings, trains them to align image patches with words, and uses an RNN to map image fragments to phrases. It also discusses Vinyals et al (2015) and Mao et al (2015), noting common themes of joint embedding spaces and differences in image processing, inputs to RNNs, and recurrence types. Evaluation metrics like R@K and Med r are also introduced.
Neural Network Activation Functions
Bharatiya Vidya Bhavan’s
Sardar Patel Institute of Technology,
Munshi Nagar, Andheri (w) Mumbai.
Neural Network Activation Functions
AICTE Sponsored Two Week FDP on
“Insights into Intelligent Automation
Machine Learning and Data science”
19th Oct to 31st Oct 2020
By
Dhananjay Kalbande
Professor, Computer
Engineering,S.P.I.T. Mumba
This document provides an overview of topics to be covered in R Programming including variables, data types, data import/export, logical statements, loops, functions, data plotting and visualization, and basic statistical functions and packages. It then goes on to introduce R, explaining that it is a programming language for statistical analysis and graphical display. It discusses why R is useful for data analysis and exploration due to its large collection of tools, ability to handle big data, and open source community support. The document also covers installing R and RStudio, defining variables, common data types like vectors, matrices, arrays, lists and data frames, and basic operations and control structures like if/else statements and loops.
This document provides an introduction to machine learning and artificial intelligence. It discusses the types of machine learning tasks including supervised learning, unsupervised learning, and reinforcement learning. It also summarizes commonly used machine learning algorithms and frameworks. Examples are given of applying machine learning to tasks like image classification, sentiment analysis, and handwritten digit recognition. Issues that can cause machine learning projects to fail are identified and approaches to addressing different machine learning problems are outlined.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
2. Learning
Study of processes that lead to self-
improvement of machine performance.
It implies the ability to use knowledge to
create new knowledge or integrating new
facts into an existing knowledge structure
Learning typically requires repetition and
practice to reduce differences between
observed and actual performance
2
3. Learning
Definition:
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.
3
4. Learning & Adaptation
4
• ”Modification of a behavioral tendency by
expertise.” (Webster 1984)
• ”A learning machine, broadly defined as any
device whose actions are influenced by past
experiences.” (Nilsson 1965)
• ”Any change in a system that allows it to
perform better the second time on repetition of
the same task or on another task drawn from
the same population.” (Simon 1983)
5. Negative Features of Human
Learning
Its slow (5-6 years for motor skills 12-20
years for abstract reasoning)
Inefficient
Expensive
There is no copy process
Learning strategy is often a function of
knowledge available to learner
5
6. Applications of ML
Learning to recognize spoken words
Learning to drive an autonomous
vehicle
Learning to classify objects
Learning to play world-class
backgammon
Designing the morphology and
control structure of electro-
mechanical artefacts 6
7. Different kinds of learning…
Supervised learning:
Someone gives us examples and the right answer for
those examples
We have to predict the right answer for unseen
examples
Unsupervised learning:
We see examples but get no feedback
We need to find patterns in the data
Reinforcement learning:
We take actions and get rewards
Have to learn how to get high rewards
8. Learning with a Teacher
supervised learning
knowledge represented by a set of input-output
examples (xi,yi)
minimize the error between the actual response of
the learner and the desired response
8
Environment Teacher
Learning
system
state x
S
desired
response
actual
response
error signal
+
-
9. Unsupervised Learning
self-organized learning
no teacher
task independent quality measure
identify regularities in the data and discover classes
automatically
9
Environment
Learning
system
state
10. Learning by Examples
10
Sky Temp Humi
d
Wind Water Fore-
cast
Enjoy
Sport
Sunny
Sunny
Rainy
Sunny
Warm
Warm
Cold
Warm
Norma
l
High
High
High
Strong
Strong
Strong
Strong
Warm
Warm
Warm
Cool
Same
Same
Chane
Chane
Yes
Yes
No
Yes
Concept: ”days on which my friend Aldo enjoys his
favourite water sports”
Task: predict the value of ”Enjoy Sport” for an
arbitrary day based on the values of the other
attributes
12. Constructing a
decision tree, one
step at a time
address?
yes no
+a, -c, +i, +e, +o, +u: Y
-a, +c, -i, +e, -o, -u: N
+a, -c, +i, -e, -o, -u: Y
-a, -c, +i, +e, -o, -u: Y
-a, +c, +i, -e, -o, -u: N
-a, -c, +i, -e, -o, +u: Y
+a, -c, -i, -e, +o, -u: N
+a, +c, +i, -e, +o, -u: N
-a, +c, -i, +e, -o, -u: N
-a, -c, +i, +e, -o, -u: Y
-a, +c, +i, -e, -o, -u: N
-a, -c, +i, -e, -o, +u: Y
+a, -c, +i, +e, +o, +u: Y
+a, -c, +i, -e, -o, -u: Y
+a, -c, -i, -e, +o, -u: N
+a, +c, +i, -e, +o, -u: N criminal? criminal?
-a, +c, -i, +e, -o, -u: N
-a, +c, +i, -e, -o, -u: N
-a, -c, +i, +e, -o, -u: Y
-a, -c, +i, -e, -o, +u: Y
+a, -c, +i, +e, +o, +u: Y
+a, -c, +i, -e, -o, -u: Y
+a, -c, -i, -e, +o, -u: N
+a, +c, +i, -e, +o, -u: N
income?
+a, -c, +i, +e, +o, +u: Y
+a, -c, +i, -e, -o, -u: Y
+a, -c, -i, -e, +o, -u: N
yes no
yes no
yes no Address was
maybe not the
best attribute
to start with…
13. Different approach: nearest neighbor(s)
Next person is -a, +c, -i, +e, -o, +u. Will we get paid back?
Nearest neighbor: simply look at most similar example in
the training data, see what happened there
+a, -c, +i, +e, +o, +u: Y (distance 4)
-a, +c, -i, +e, -o, -u: N (distance 1)
+a, -c, +i, -e, -o, -u: Y (distance 5)
-a, -c, +i, +e, -o, -u: Y (distance 3)
-a, +c, +i, -e, -o, -u: N (distance 3)
-a, -c, +i, -e, -o, +u: Y (distance 3)
+a, -c, -i, -e, +o, -u: N (distance 5)
+a, +c, +i, -e, +o, -u: N (distance 5)
Nearest neighbor is second, so predict N
k nearest neighbors: look at k nearest neighbors, take a vote
E.g., 5 nearest neighbors have 3 Ys, 2Ns, so predict Y
14. Approaches to Machine Learning
Numerical approaches
Build numeric model with parameters based on
successes
Structural approaches
Concerned with the process of defining
relationships by creating links between
concepts
14
15. Learning methods
Decision rules:
If income < $30.000 then reject
Bayesian network:
P(good | income, credit history,….)
Neural Network:
Nearest Neighbor:
Take the same decision as for the customer in
the data base that is most similar to the
applicant
15
16. 16
Classification
Assign object/event to one of a given finite set of
categories.
Medical diagnosis
Credit card applications or transactions
Fraud detection in e-commerce
Worm detection in network packets
Spam filtering in email
Recommended articles in a newspaper
Recommended books, movies, music, or jokes
Financial investments
DNA sequences
Spoken words
Handwritten letters
Astronomical images
17. 17
Problem Solving / Planning /
Control
Performing actions in an environment in order
to achieve a goal.
Solving calculus problems
Playing checkers, chess, or backgammon
Balancing a pole
Driving a car or a jeep
Flying a plane, helicopter, or rocket
Controlling an elevator
Controlling a character in a video game
Controlling a mobile robot
18. Another Example:
Handwriting Recognition
Background concepts:
Pixel information
Categorisations:
(Matrix, Letter) pairs
Both positive &
negative
Task
Correctly categorise
An unseen example
Into 1 of 26 categories
Positive:
– This is a letter S:
Negative:
– This is a letter Z:
19. Genetic Algorithm
Genetic Algorithms have been applied
successfully to a variety of AI
applications
For example, they have been used to
learn collections of rules for robot
control.
Genetic Algorithms and genetic
programming are called Evolutionary
Computation
20. Theory of Evolution
Every organism has unique attributes
that can be transmitted to its offspring
Selective breeding can be used to manage
changes from one generation to the next
Nature applies certain pressures that
cause individuals to evolve over time
21. Genetic Algorithms (GAs) and
Genetic Programming (GP)
Genetic Algorithms
Optimising parameters for problem solving
Represent the parameters in the solution(s)
As a “bit” string normally, but often something else
Evolve answers in this representation
Genetic Programming
Representation of solutions is richer in general
Solutions can be interpreted as programs
Evolutionary process is very similar
22. GA
Genetic algorithms provide an AI method
by an analogy of biological evolution
It constructs a population of evolving
solutions to solve the problem
23. Genetic Algorithms
What are they?
Evolutionary algorithms that make use of operations
like mutation, recombination, and selection
Uses?
Difficult search problems
Optimization problems
Machine learning
Adaptive rule-bases
24. Classical GAs
Representation of parameters is a bit string
Solutions to a problem represented in binary
101010010011101010101
Start with a population (fairly large set)
Of possible solutions known as individuals
Combine possible solutions by swapping material
Choose the “best” solutions to swap material between
and kill off the worse solutions
This generates a new set of possible solutions
Requires a notion of “fitness” of the individual
Base on an evaluation function with respect to the
problem
26. Genotype space =
{0,1}L
Phenotype space
Encoding
(representation)
Decoding
(inverse representation)
011101001
010001001
10010010
10010001
Representation
27. GA Representation
Genetic algorithms are represented as
gene
Each population consists of a whole set
of genes
Using biological reproduction, new
population is created from old one.
28. The Initial Population
Represent solutions to problems
As a bit string of length L
Choose an initial population size
Generate length L strings of 1s & 0s
randomly
Strings are sometimes called
chromosomes
Letters in the string are called “genes”
We call the bit-string “individuals”
29. Initialization
Initial population must be a
representative sample of the search space
Random initialization can be a good idea
(if the sample is large enough)
30. The gene
Each gene in the population is
represented by bit strings.
001 10 10
Outlook Wind play tennis
0011010
31. Gene Example
The idea is to use a bit string to describe
the value of attribute
The attribute Outlook has 3 values
(sunny, overcast, raining)
So we use 3 bit length to represent
attribute outlook
010 represent the outlook = overcast
32. GA
The fitness function evaluates each
solution and decide it will be in next
generation of solutions
33. Selection
Want to to give preference to “better”
individuals to add to mating pool
If entire population ends up being selected
it may be desirable to conduct a tournament
to order individuals in population
Would like to keep the best in the mating
pool and drop the worst
35. Rank Selection
All individuals are sorted according to
their fitness.
Each individual is then assigned a
probability of being selected from some
prior probability density.
36. Main idea: better individuals get higher chance
Chances proportional to fitness
Implementation: roulette wheel technique
Assign to each individual a part of the
roulette wheel
Spin the wheel n times to select n
individuals
Selection
fitness(A) = 3
fitness(B) = 1
fitness(C) = 2
A C
1/6 = 17%
3/6 = 50%
B
2/6 = 33%
39. New Population
To build new population from old one we
use genetic operators to evolve the
population of solutions
Genetic operators are
Crossover operator
Mutation operator
Production operator
40. Crossover operator
It produces two new offspring from two
parent strings by copying selected bits
from each parent.
11101001000
00001010101
00001001000
11101010101
41. Crossover
In sexual reproduction the genetic codes
of both parents are combined to create
offspring
Would like to keep 60/40 split between
parent contributions
95/5 splits negate the benefits of
crossover (too much like asexual
reproduction)
42. Mutation operator
It produces offspring from single parent
by small random change in bit string.
11101001000
11100001000
43. Mutation
Mutation is important for maintaining diversity in
the genetic code
In humans, mutation was responsible for the
evolution of intelleigence
Example: The occasional (low probably)
alteration of a bit position in a string
44. Replacement
Determine when to insert new offspring
into the population and which individuals
to drop out based on fitness
Steady state evolution calls for the same
number of individuals in the population,
so each new offspring processed one at a
time so fit individuals can remain a long
time
45. The Termination Check
Termination may involve testing whether an individual
solves the problem to a good enough standard
Not necessarily having found a definitive answer
Alternatively, termination may occur after a fixed time
Or after a fixed number of generations
Note that the best individual in a population
So, your GA should:
Record the best from each generation
Output the best from all populations as the answer
46. Why use genetic algorithms?
They can solve hard problems
Easy to interface genetic algorithms to existing
simulations and models
GA’s are extensible
GA’s are easy to hybridize
GA’s work by sampling, so populations can be
sized to detect differences with specified error
rates
Use little problem specific code
47. Example
No. A1 A2 Classification
1 T T +
2 T T +
3 T F -
4 F F +
5 F T -
6 F T -
53. GA Application
Searching maximum of function
Search for value of x to y=f(x) be
maximum.
X play the role of genes, binary code of x
value in 8 bits gene (chromosome)
Y the role of fitness
54. GA Application
Given the digits 0 through 9 and
operators +, -, *, and /.
Find sequence that represent given target
number.
E.g. Given number 23, the sequence
+6+5*4/2+1
If number is 75.5, then 5/2+9*7-5
55. GA Application
Four bits are required to represent char
0:0000, …….9:1001, +:1010, -:1011,
*:1100, /:1101
Gene:0110101001011100010011010010
10100001 is 6+5*4/2+1 = 23
56. TSP Application
Use a genetic algorithm to solve the traveling
salesman problem we could begin by creating a
population of candidate solutions
We need to define mutation, crossover, and
selection methods to aid in evolving a solution
from this population
58. Automatic Programming
Getting software to write software
Brilliant idea, but turned out to be very difficult
Intuitively, should be easier than other tasks
The automatic programming community
Didn’t deliver on early promises
So people began to avoid this area
And “Automated Programming” became words of
warning
59. Questions to be Answered
How is the programming task specified?
How are the ingredients chosen/specified/
How do we represent programs?
Which enables reproduction, evaluation, etc.
How are individuals chosen for reproduction?
How are offspring produced?
How are termination conditions specified?
We will start by looking at the representation of programs,
as this is required to answer many questions
60. Genetic Programming
It same as GA but the gene is tree
Each tree = computer program
It contain a population of computer
programs to solve the problem
specifically
61. A Tree Representation
Idea: represent programs as trees
Each node is either a function or a terminal
Functions
Examples: add, multiply, square root, sine, cosine
Problem specific ones: getPixel(), goLeft()
Terminals
Constants and variables, e.g., 10, 17, X, Y
The nodes below a function node
Are the inputs to that function
The node itself represents the output from the function
As input to the parent node
The nodes at the ends of branches are terminals only
63. “Breeding” Computer Programs
Start off with a large “pool” of random
computer programs.
Need a way of coming up with the best solution
to the problem using the programs in the “pool”
Based on the definition of the problem and
criteria specified in the fitness test, mutations
and crossovers are used to come up with new
programs which will solve the problem.
64. Tree based representation
Trees are a universal form, e.g. consider
Arithmetic formula
Logical formula
Program
1
5
)
3
(
2
y
x
(x true) (( x y ) (z (x y)))
i =1;
while (i < 20)
{
i = i +1
}
72. Application Domain #1
Evolving Electronic Circuits
John Koza
Stanford Professor and CEO of “Genetic
Programming Inc.”
Guru of Genetic Programming, very successful
Made GP more mainstream by his success
Particularly fruitful area:
Producing designs for circuit diagrams
E.g., antennas, amplifiers, etc.
Functions mimic how transistors, resistors, etc. work
73. GP Application 1
Given the digits 0 through 9 and
operators +, -, *, and /. Find sequence
that represent given target number.
Given number 23, the sequence
+6+5*4/2+1
If number is 75.5, then 5/2+9*7-5
74. Generate Random Programs
Generate well-formed formulas using
prefix notation from variable A and a set
of function symbols, for example { +,
-, *, %, sqrt }
Examples:
( + A ( * ( sqrt A ) A ) )
(* A (- (* A A) (sqrt A) ) )
75. Examples of Genetic Programs
Symbolic Regression - the process of
discovering both the functional form of a
target function and all of its necessary
coefficients, or at least an approximation to
these.
Analog circuit design - Embryo circuit -
Initial circuit which is modified to create a
new circuit according to functionality
criteria.