This chapter discusses supervised machine learning. It begins by explaining the concept of learning a class from labeled examples to make predictions. This involves finding a description that classifies the positive and negative examples. The chapter then discusses key concepts like the version space that contains all hypotheses consistent with the training data, and the VC dimension which measures a hypothesis space's capacity. It also covers techniques like probably approximately correct learning for generalization. The chapter outlines different types of supervised learning problems like classification of multiple classes and regression to predict numeric values.
Regression, Bayesian Learning and Support vector machineDr. Radhey Shyam
The document discusses machine learning techniques including regression, Bayesian learning, and support vector machines. It provides details on linear regression, logistic regression, Bayes' theorem, concept learning, the Bayes optimal classifier, naive Bayes classifier, and Bayesian belief networks. The document is a slide presentation given by Dr. Radhey Shyam on machine learning techniques, outlining these various topics in greater detail over multiple slides.
Get best thesis topics in machine learning from Experienced Ph.D. Writers at Techsparks with 100% Plagiarism Free Work & Affordable price. Our goal is to make students free from their assignments burden, by providing the best thesis assistance. For more details call us at-9465330425 or Visit at: https://bit.ly/3zRB3vN
Abstract: This PDSG workshop introduces basic concepts of ensemble methods in machine learning. Concepts covered are Condercet Jury Theorem, Weak Learners, Decision Stumps, Bagging and Majority Voting.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
In this tutorial, we will learn the the following topics -
+ Voting Classifiers
+ Bagging and Pasting
+ Random Patches and Random Subspaces
+ Random Forests
+ Boosting
+ Stacking
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Simplilearn
This document provides an overview of machine learning, including:
- Machine learning allows computers to learn from data without being explicitly programmed, through processes like analyzing data, training models on past data, and making predictions.
- The main types of machine learning are supervised learning, which uses labeled training data to predict outputs, and unsupervised learning, which finds patterns in unlabeled data.
- Common supervised learning tasks include classification (like spam filtering) and regression (like weather prediction). Unsupervised learning includes clustering, like customer segmentation, and association, like market basket analysis.
- Supervised and unsupervised learning are used in many areas like risk assessment, image classification, fraud detection, customer analytics, and more
1. The Naive Bayes classifier is a simple probabilistic classifier based on Bayes' theorem that assumes independence between features.
2. It has various applications including email spam detection, language detection, and document categorization.
3. The Naive Bayes approach involves computing the class prior probabilities, feature likelihoods, and applying Bayes' theorem to calculate the posterior probabilities to classify new instances. Laplace smoothing is often used to handle cases with insufficient training data.
This Naive Bayes Tutorial from Edureka will help you understand all the concepts of Naive Bayes classifier, use cases and how it can be used in the industry. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their concepts in Data Science and Machine Learning through Naive Bayes. Below are the topics covered in this tutorial:
1. What is Machine Learning?
2. Introduction to Classification
3. Classification Algorithms
4. What is Naive Bayes?
5. Use Cases of Naive Bayes
6. Demo – Employee Salary Prediction in R
Regression, Bayesian Learning and Support vector machineDr. Radhey Shyam
The document discusses machine learning techniques including regression, Bayesian learning, and support vector machines. It provides details on linear regression, logistic regression, Bayes' theorem, concept learning, the Bayes optimal classifier, naive Bayes classifier, and Bayesian belief networks. The document is a slide presentation given by Dr. Radhey Shyam on machine learning techniques, outlining these various topics in greater detail over multiple slides.
Get best thesis topics in machine learning from Experienced Ph.D. Writers at Techsparks with 100% Plagiarism Free Work & Affordable price. Our goal is to make students free from their assignments burden, by providing the best thesis assistance. For more details call us at-9465330425 or Visit at: https://bit.ly/3zRB3vN
Abstract: This PDSG workshop introduces basic concepts of ensemble methods in machine learning. Concepts covered are Condercet Jury Theorem, Weak Learners, Decision Stumps, Bagging and Majority Voting.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
In this tutorial, we will learn the the following topics -
+ Voting Classifiers
+ Bagging and Pasting
+ Random Patches and Random Subspaces
+ Random Forests
+ Boosting
+ Stacking
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Simplilearn
This document provides an overview of machine learning, including:
- Machine learning allows computers to learn from data without being explicitly programmed, through processes like analyzing data, training models on past data, and making predictions.
- The main types of machine learning are supervised learning, which uses labeled training data to predict outputs, and unsupervised learning, which finds patterns in unlabeled data.
- Common supervised learning tasks include classification (like spam filtering) and regression (like weather prediction). Unsupervised learning includes clustering, like customer segmentation, and association, like market basket analysis.
- Supervised and unsupervised learning are used in many areas like risk assessment, image classification, fraud detection, customer analytics, and more
1. The Naive Bayes classifier is a simple probabilistic classifier based on Bayes' theorem that assumes independence between features.
2. It has various applications including email spam detection, language detection, and document categorization.
3. The Naive Bayes approach involves computing the class prior probabilities, feature likelihoods, and applying Bayes' theorem to calculate the posterior probabilities to classify new instances. Laplace smoothing is often used to handle cases with insufficient training data.
This Naive Bayes Tutorial from Edureka will help you understand all the concepts of Naive Bayes classifier, use cases and how it can be used in the industry. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their concepts in Data Science and Machine Learning through Naive Bayes. Below are the topics covered in this tutorial:
1. What is Machine Learning?
2. Introduction to Classification
3. Classification Algorithms
4. What is Naive Bayes?
5. Use Cases of Naive Bayes
6. Demo – Employee Salary Prediction in R
Stacking involves training a combiner algorithm to make a final prediction using the predictions of multiple other learning algorithms as inputs. The other algorithms are first trained on the available data, then a combiner algorithm is trained using the predictions from the other algorithms to make the final prediction. Stacking uses different feature selection and model combinations as base learners, whose results are then input into a meta learner to produce the final result.
PCA is used to reduce the dimensionality of image data by finding the principal components that account for the most variance in the data. It works by constructing a feature space from the eigenvectors of the image covariance matrix. Images are represented as vectors in this lower-dimensional feature space rather than the original high-dimensional pixel space. The eigenvectors corresponding to the largest eigenvalues are the principal components or "eigenfaces" that best represent the variation between images. New images can be classified by projecting them into this eigenface feature space.
Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation
3D Brain Tumor Segmentation Scheme using K-mean Clustering and Connected Component Labeling Algorithms
Volume Identification and Estimation of MRI Brain Tumor
MRI Breast cancer diagnosis hybrid approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier
House Price Prediction An AI Approach.Nahian Ahmed
Suppose you have a house. And you want to sell it. Through House Price Prediction project you can predict the price from previous sell history.
And we make this prediction using Machine Learning.
This document discusses using MATLAB to detect breast cancer through analysis of mammogram and thermal images. It introduces breast cancer and explains that early detection is key to successful treatment. Currently, mammography and thermography are used for detection, but mammography has weaknesses like pain and radiation. The purpose of this project is to design a system to detect signs in mammogram and thermal images using image processing techniques in MATLAB. Mammogram images will be analyzed using morphology before feature extraction and classification. Thermal images will have features extracted from the heat distribution to identify possible cancer areas.
Ensemble methods combine multiple machine learning models to obtain better predictive performance than from any individual model. There are two main types of ensemble methods: sequential (e.g AdaBoost) where models are generated one after the other, and parallel (e.g Random Forest) where models are generated independently. Popular ensemble methods include bagging, boosting, and stacking. Bagging averages predictions from models trained on random samples of the data, while boosting focuses on correcting previous models' errors. Stacking trains a meta-model on predictions from other models to produce a final prediction.
This slide includes :
Types of Machine Learning
Supervised Learning
Brain
Neuron
Design a Learning System
Perspectives
Issues in Machine Learning
Learning Task
Learning as Search
Hypothesis
Version Spaces
Candidate elimination algorithm
linear Discriminant
Perception
Linear Separability
Linear Regression
Unsupervised Learning
Reinforcement Learning
Evolutionary Learning
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal
Linear Regression Algorithm | Linear Regression in Python | Machine Learning ...Edureka!
The document discusses linear regression algorithms. It begins with an introduction to regression analysis and its uses. Then it differentiates between linear and logistic regression. Next, it defines linear regression and discusses how to find the best fit regression line using the least squares method. It also explains how to check the goodness of fit using the R-squared method. Finally, it provides an overview of implementing linear regression using Python libraries.
Clustering:k-means, expect-maximization and gaussian mixture modeljins0618
This document discusses K-means clustering, Expectation Maximization (EM), and Gaussian mixture models (GMM). It begins with an overview of unsupervised learning and introduces K-means as a simple clustering algorithm. It then describes EM as a general algorithm for maximum likelihood estimation that can be applied to problems like GMM. GMM is presented as a density estimation technique that models data using a weighted sum of Gaussian distributions. EM is described as a method for estimating the parameters of a GMM from data.
The document provides an overview of different machine learning algorithms used to predict house sale prices in King County, Washington using a dataset of over 21,000 house sales. Linear regression, neural networks, random forest, support vector machines, and Gaussian mixture models were applied. Neural networks with 100 hidden neurons performed best with an R-squared of 0.9142 and RMSE of 0.0015. Random forest had an R-squared of 0.825. Support vector machines achieved 73% accuracy. Gaussian mixture modeling clustered homes into three groups and achieved 49% accuracy.
Machine learning works by processing data to discover patterns that can be used to analyze new data. Popular programming languages for machine learning include Python, R, and SQL. There are several types of machine learning including supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and deep learning. Common machine learning tasks involve classification, regression, clustering, dimensionality reduction, and model selection. Machine learning is widely used for applications such as spam filtering, recommendations, speech recognition, and machine translation.
Supervised Machine Learning With Types And TechniquesSlideTeam
Supervised Machine Learning with Types and Techniques is for the mid level managers giving information about what is supervised machine learning, its types, how supervised machine learning, its advantages. You can also know the difference between Supervised and Unsupervised Machine learning to understand supervised machine learning in a better way for business growth. https://bit.ly/3ewivHm
This document provides an overview of key mathematical concepts relevant to machine learning, including linear algebra (vectors, matrices, tensors), linear models and hyperplanes, dot and outer products, probability and statistics (distributions, samples vs populations), and resampling methods. It also discusses solving systems of linear equations and the statistical analysis of training data distributions.
This document discusses machine learning concepts like supervised and unsupervised learning. It explains that supervised learning uses known inputs and outputs to learn rules while unsupervised learning deals with unknown inputs and outputs. Classification and regression are described as types of supervised learning problems. Classification involves categorizing data into classes while regression predicts continuous, real-valued outputs. Examples of classification and regression problems are provided. Classification models like heuristic, separation, regression and probabilistic models are also mentioned. The document encourages learning more about classification algorithms in upcoming videos.
Machine Learning With Logistic RegressionKnoldus Inc.
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
This knolx is about an introduction to machine learning, wherein we see the basics of various different algorithms. This knolx isn't a complete intro to ML but can be a good starting point for anyone who wants to start in ML. In the end, we will take a look at the demo wherein we will analyze the FIFA dataset going through the understanding of various data analysis techniques and use an ML algorithm to derive 5 players that are similar to each other.
Breast cancer detection using Artificial Neural NetworkSubroto Biswas
This presentation summarizes research on diagnosing breast cancer using an artificial neural network. It begins with an introduction of the topic and presenter. The contents include descriptions of breast cancer, artificial neural networks, and backpropagation. It then details the breast cancer database used, the neural network model developed, and its performance in diagnosing cancers as benign or malignant. The conclusion is that neural networks show potential for medical diagnosis but require further optimization. Suggested future work includes exploring other training methods, feature selection, and adding treatment recommendations.
The document discusses machine learning concepts and algorithms. It provides definitions of learning, discusses different types of learning problems including supervised, unsupervised, and reinforcement learning. It also covers key machine learning topics like generalization error, empirical risk minimization, probably approximately correct learning, model selection, and overfitting vs underfitting.
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.
Stacking involves training a combiner algorithm to make a final prediction using the predictions of multiple other learning algorithms as inputs. The other algorithms are first trained on the available data, then a combiner algorithm is trained using the predictions from the other algorithms to make the final prediction. Stacking uses different feature selection and model combinations as base learners, whose results are then input into a meta learner to produce the final result.
PCA is used to reduce the dimensionality of image data by finding the principal components that account for the most variance in the data. It works by constructing a feature space from the eigenvectors of the image covariance matrix. Images are represented as vectors in this lower-dimensional feature space rather than the original high-dimensional pixel space. The eigenvectors corresponding to the largest eigenvalues are the principal components or "eigenfaces" that best represent the variation between images. New images can be classified by projecting them into this eigenface feature space.
Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation
3D Brain Tumor Segmentation Scheme using K-mean Clustering and Connected Component Labeling Algorithms
Volume Identification and Estimation of MRI Brain Tumor
MRI Breast cancer diagnosis hybrid approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier
House Price Prediction An AI Approach.Nahian Ahmed
Suppose you have a house. And you want to sell it. Through House Price Prediction project you can predict the price from previous sell history.
And we make this prediction using Machine Learning.
This document discusses using MATLAB to detect breast cancer through analysis of mammogram and thermal images. It introduces breast cancer and explains that early detection is key to successful treatment. Currently, mammography and thermography are used for detection, but mammography has weaknesses like pain and radiation. The purpose of this project is to design a system to detect signs in mammogram and thermal images using image processing techniques in MATLAB. Mammogram images will be analyzed using morphology before feature extraction and classification. Thermal images will have features extracted from the heat distribution to identify possible cancer areas.
Ensemble methods combine multiple machine learning models to obtain better predictive performance than from any individual model. There are two main types of ensemble methods: sequential (e.g AdaBoost) where models are generated one after the other, and parallel (e.g Random Forest) where models are generated independently. Popular ensemble methods include bagging, boosting, and stacking. Bagging averages predictions from models trained on random samples of the data, while boosting focuses on correcting previous models' errors. Stacking trains a meta-model on predictions from other models to produce a final prediction.
This slide includes :
Types of Machine Learning
Supervised Learning
Brain
Neuron
Design a Learning System
Perspectives
Issues in Machine Learning
Learning Task
Learning as Search
Hypothesis
Version Spaces
Candidate elimination algorithm
linear Discriminant
Perception
Linear Separability
Linear Regression
Unsupervised Learning
Reinforcement Learning
Evolutionary Learning
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal
Linear Regression Algorithm | Linear Regression in Python | Machine Learning ...Edureka!
The document discusses linear regression algorithms. It begins with an introduction to regression analysis and its uses. Then it differentiates between linear and logistic regression. Next, it defines linear regression and discusses how to find the best fit regression line using the least squares method. It also explains how to check the goodness of fit using the R-squared method. Finally, it provides an overview of implementing linear regression using Python libraries.
Clustering:k-means, expect-maximization and gaussian mixture modeljins0618
This document discusses K-means clustering, Expectation Maximization (EM), and Gaussian mixture models (GMM). It begins with an overview of unsupervised learning and introduces K-means as a simple clustering algorithm. It then describes EM as a general algorithm for maximum likelihood estimation that can be applied to problems like GMM. GMM is presented as a density estimation technique that models data using a weighted sum of Gaussian distributions. EM is described as a method for estimating the parameters of a GMM from data.
The document provides an overview of different machine learning algorithms used to predict house sale prices in King County, Washington using a dataset of over 21,000 house sales. Linear regression, neural networks, random forest, support vector machines, and Gaussian mixture models were applied. Neural networks with 100 hidden neurons performed best with an R-squared of 0.9142 and RMSE of 0.0015. Random forest had an R-squared of 0.825. Support vector machines achieved 73% accuracy. Gaussian mixture modeling clustered homes into three groups and achieved 49% accuracy.
Machine learning works by processing data to discover patterns that can be used to analyze new data. Popular programming languages for machine learning include Python, R, and SQL. There are several types of machine learning including supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and deep learning. Common machine learning tasks involve classification, regression, clustering, dimensionality reduction, and model selection. Machine learning is widely used for applications such as spam filtering, recommendations, speech recognition, and machine translation.
Supervised Machine Learning With Types And TechniquesSlideTeam
Supervised Machine Learning with Types and Techniques is for the mid level managers giving information about what is supervised machine learning, its types, how supervised machine learning, its advantages. You can also know the difference between Supervised and Unsupervised Machine learning to understand supervised machine learning in a better way for business growth. https://bit.ly/3ewivHm
This document provides an overview of key mathematical concepts relevant to machine learning, including linear algebra (vectors, matrices, tensors), linear models and hyperplanes, dot and outer products, probability and statistics (distributions, samples vs populations), and resampling methods. It also discusses solving systems of linear equations and the statistical analysis of training data distributions.
This document discusses machine learning concepts like supervised and unsupervised learning. It explains that supervised learning uses known inputs and outputs to learn rules while unsupervised learning deals with unknown inputs and outputs. Classification and regression are described as types of supervised learning problems. Classification involves categorizing data into classes while regression predicts continuous, real-valued outputs. Examples of classification and regression problems are provided. Classification models like heuristic, separation, regression and probabilistic models are also mentioned. The document encourages learning more about classification algorithms in upcoming videos.
Machine Learning With Logistic RegressionKnoldus Inc.
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
This knolx is about an introduction to machine learning, wherein we see the basics of various different algorithms. This knolx isn't a complete intro to ML but can be a good starting point for anyone who wants to start in ML. In the end, we will take a look at the demo wherein we will analyze the FIFA dataset going through the understanding of various data analysis techniques and use an ML algorithm to derive 5 players that are similar to each other.
Breast cancer detection using Artificial Neural NetworkSubroto Biswas
This presentation summarizes research on diagnosing breast cancer using an artificial neural network. It begins with an introduction of the topic and presenter. The contents include descriptions of breast cancer, artificial neural networks, and backpropagation. It then details the breast cancer database used, the neural network model developed, and its performance in diagnosing cancers as benign or malignant. The conclusion is that neural networks show potential for medical diagnosis but require further optimization. Suggested future work includes exploring other training methods, feature selection, and adding treatment recommendations.
The document discusses machine learning concepts and algorithms. It provides definitions of learning, discusses different types of learning problems including supervised, unsupervised, and reinforcement learning. It also covers key machine learning topics like generalization error, empirical risk minimization, probably approximately correct learning, model selection, and overfitting vs underfitting.
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.
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.
This document provides an introduction to machine learning, including:
1) Definitions of learning as improving performance from experience and classification tasks.
2) Reasons to study machine learning like engineering better systems and discovering new knowledge.
3) Key aspects of designing a learning system like choosing the task, experience, and representation.
This document discusses machine learning concepts including what learning is, different types of learning tasks like classification and problem solving/planning, measuring performance, reasons to study machine learning, related disciplines, defining learning tasks, designing learning systems, sample learning problems, and lessons learned about learning. It uses the example of learning to play checkers to illustrate many of these concepts such as representing the target function, obtaining training data, choosing a learning algorithm, and discussing specific algorithms like least mean squares regression.
This document introduces machine learning and supervised learning. It discusses learning a classifier from labeled examples to predict a target variable. The key points covered are:
- Supervised learning involves learning a function that maps inputs to outputs from a set of labeled examples.
- The goal is to learn a hypothesis h that has low error on the training set and generalizes well to new examples.
- The version space is the set of all hypotheses consistent with the training data.
- Controlling the complexity of the hypothesis class H via measures like VC dimension can improve generalization.
- For classification, multiple target classes are handled by learning one hypothesis per class. Regression learns a real-valued target.
- There is a tradeoff
This document introduces machine learning and supervised learning. It discusses learning a classifier from labeled examples to predict a target variable. The key points covered are:
- Supervised learning involves learning a function that maps inputs to outputs from example input-output pairs.
- The goal is to learn a hypothesis h that has low error on the training set and generalizes well to new examples.
- The version space is the set of all hypotheses consistent with the training data.
- Controlling the complexity of the hypothesis class H via measures like VC dimension can improve generalization.
- For classification, multiple target classes are handled by learning one hypothesis per class. Regression learns a real-valued target function.
- There is a trade
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.
The document discusses machine learning and various related concepts. It provides an overview of machine learning, including well-posed learning problems, designing learning systems, supervised learning, and different machine learning approaches. It also discusses specific machine learning algorithms like naive Bayes classification and decision tree learning.
The document provides an overview of machine learning concepts including:
- Defining machine learning as computer algorithms that improve with experience and data
- Describing examples like speech recognition, medical diagnosis, and game playing
- Outlining the components of designing a machine learning system like choosing the target function, representation, and learning algorithm
- Explaining concepts like version spaces, decision trees, and neural networks that are used in machine learning applications
This document discusses supervised learning. It begins with an introduction explaining that supervised learning aims to learn a mapping from inputs to outputs whose correct values are provided by a supervisor. It then provides an example of using classification to find a family car based on attributes like price and engine capacity. The document goes on to define key concepts in supervised learning including positive and negative examples, the actual hypothesis, error calculation, generalization, and probably approximately correct learning.
Deep Learning: Introduction & Chapter 5 Machine Learning BasicsJason Tsai
Given lecture for Deep Learning 101 study group with Frank Wu on Dec. 9th, 2016.
Reference: https://www.deeplearningbook.org/
Initiated by Taiwan AI Group (https://www.facebook.com/groups/Taiwan.AI.Group/)
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.
Machine Learning: Decision Trees Chapter 18.1-18.3butest
The document discusses machine learning and decision trees. It provides an overview of different machine learning paradigms like rote learning, induction, clustering, analogy, discovery, and reinforcement learning. It then focuses on decision trees, describing them as trees that classify examples by splitting them along attribute values at each node. The goal of learning decision trees is to build a tree that can accurately classify new examples. It describes the ID3 algorithm for constructing decision trees in a greedy top-down manner by choosing the attribute that best splits the training examples at each node.
This document appears to be an exam for a course on machine learning or artificial intelligence. It contains instructions for taking the exam, which is closed book and does not allow calculators. It then lists 6 multiple choice or short answer questions worth various point values that assess knowledge of topics like linear separators, sources of error in machine learning models, designing neural network architectures, and radial basis feature transformations.
This document discusses machine learning and natural language processing (NLP) techniques for text classification. It provides an overview of supervised vs. unsupervised learning and classification vs. regression problems. It then walks through the steps to perform binary text classification using logistic regression and Naive Bayes models on an SMS spam collection dataset. The steps include preparing and splitting the data, numerically encoding text with Count Vectorization, fitting models on the training data, and evaluating model performance on the test set using metrics like accuracy, precision, recall and F1 score. Naive Bayes classification is also introduced as an alternative simpler technique to logistic regression for text classification tasks.
Understanding Blackbox Prediction via Influence FunctionsSEMINARGROOT
Pang Wei Koh and Percy Liang
"Understanding Black-Box prediction via influence functions" ICML 2017 Best paper
References:
https://youtu.be/0w9fLX_T6tY
https://arxiv.org/abs/1703.04730
The candidate elimination algorithm incrementally builds the version space by processing examples one by one. Each example may shrink the version space by removing hypotheses inconsistent with that example. It does this by updating the general and specific boundaries for each new example. Positive examples generalize the hypotheses space, while negative examples make the hypotheses more specific. The algorithm initializes with fully general hypotheses and processes examples to iteratively refine the version space between fully general and fully specific hypotheses.
Logistic regression is a machine learning classification algorithm used to predict the probability of a categorical dependent variable given one or more independent variables. It uses a logit link function to transform the probability values into odds ratios between 0 and infinity. The model is trained by minimizing a cost function called logistic loss using gradient descent optimization. Model performance is evaluated using metrics like accuracy, precision, recall, and the confusion matrix, and can be optimized by adjusting the probability threshold for classifications.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
2. Machine Learning
Machine learning is a subset of AI, which enables the
machine to automatically learn from data, improve
performance from past experiences, and make
predictions.
Machine learning contains a set of algorithms that work on
a huge amount of data.
Data is fed to these algorithms to train them, and on the
basis of training, they build the model & perform a specific
task.
These ML algorithms help to solve different business
problems like Regression, Classification, Forecasting,
Clustering, and Associations, etc.
3. Based on the methods and way of learning,
machine learning is divided into mainly four types,
which are:
Supervised Machine Learning
Unsupervised Machine Learning
Semi-Supervised Machine Learning
Reinforcement Learning
Types of Machine Learning
4.
5. Outline of this chapter
Learning a Class from Examples,
Vapnik-Chervonenkis Dimension,
Probably Approximately Correct Learning,
Noise,
Learning Multiple Classes,
Regression,
Model Selection and Generalization,
Dimensions of a Supervised Machine Learning Algorithm
6. Learning a Class from Examples
Let us say we want to learn the class, C, of a “family
car.”
We have a set of examples of cars, and we have a
group of people that we survey to whom we show
these cars.
The people look at the cars and label them; the cars
that they believe are family cars are positive
examples, and the other cars are negative examples.
Class learning is finding a description that is shared
by all the positive examples and none of the negative
examples.
7. Doing this, we can make a prediction: Given a car that we
have not seen before, by checking with the description
learned, we will be able to say whether it is a family car or
not.
Or we can do knowledge extraction: This study may be
sponsored by a car company, and the aim may be to
understand what people expect from a family car.
After some discussions with experts in the field, let us say
that we reach the conclusion that among all features a car
may have, the features that separate a family car from
other type of cars are the price and engine power.
8. These two attributes are the inputs to the class recognizer.
Note that when we decide on this particular input
representation, we are ignoring various other attributes as
irrelevant.
Though one may think of other attributes such as seating
capacity and color that might be important for distinguishing
among car types, we will consider only price and engine
power to keep this example simple.
9. Class C of a “family car”
Prediction: Is car x a family car?
Knowledge extraction: What do people expect from a
family car?
Input representation:
x1: price, x2 : engine power
Output:
Positive (+) and negative (–) examples
10. Training set for the class of a “family car.”
Each data point corresponds to one example car, and the
coordinates of the point indicate the price and engine
power of that car.
‘+’ denotes a positive example of the class (a family car),
and
‘−’ denotes a negative example (not a family car); it is
another type of car.
Let us denote price as the first input attribute x1 and
engine power as the second attribute x2 .
Thus we represent each car using two numeric values
11. Our training data can now be plotted in the two-dimensional
(x1, x2) space where each instance t is a data point at
coordinates and its type, namely, positive versus
negative, is given by
After further discussions with the expert and the analysis of
the data, we may have reason to believe that for a car to be
a family car, its price and engine power should be in a
certain range.
(p1 ≤ price ≤ p2) AND (e1 ≤ engine power ≤ e2)
for suitable values of p1, p2, e1, and e2.
The above equation fixes H, the hypothesis class from which we
believe C is drawn, namely, the set of rectangles.
The learning algorithm then finds hypothesis the particular
hypothesis, h ∈ H, specified by a particular quadruple of (ph1, ph2,
eh1, eh2), to approximate C as closely as possible.
12. Training set X N
t
t
t
,r 1
}
{
x
X
negative
is
if
0
positive
is
if
1
x
x
r
2
1
x
x
x
13. Class C
2
1
2
1 power
engine
AND
price e
e
p
p
15. S, G, and the Version Space
most specific hypothesis, S
most general hypothesis, G
h H, between S and G is
consistent
and make up the
version space
(Mitchell, 1997)
16. One possibility is to find the most specific hypothesis, S,
that is the tightest rectangle that includes all the positive
examples and none of the negative examples.
The most general hypothesis, G, is the largest rectangle
we can draw that includes all the positive examples and
none of the negative examples.
Any h∈H between S and G is a valid hypothesis with no
error, said to be consistent with the training set, and such h
make up the version space.
17. Candidate Elimination Algorithm
The candidate elimination algorithm incrementally builds the
version space given a hypothesis space H and a set E of
examples.
The examples are added one by one; each example
possibly shrinks the version space by removing the
hypotheses that are inconsistent with the example.
The candidate elimination algorithm does this by updating
the general and specific boundary for each new example.
18. Terms Used:
Concept learning:
• Concept learning is basically learning task of the machine
(Learn by Train data)
General Hypothesis:
• Not Specifying features to learn the machine.
• G = {‘?’, ‘?’,’?’,’?’…}: Number of attributes
Specific Hypothesis:
• Specifying features to learn machine (Specific feature)
• S= {‘pi’,’pi’,’pi’…}: Number of pi depends on number of
attributes.
Version Space:
• It is intermediate of general hypothesis and Specific
hypothesis.
19. Algorithm:
Step1: Load Data set
Step2: Initialize General Hypothesis and Specific Hypothesis.
Step3: For each training example
Step4: If example is positive example
if attribute_value == hypothesis_value:
Do nothing
else:
replace attribute value with '?’
(Basically generalizing it)
Step5: If example is Negative example
Make generalize hypothesis more specific.
20. Example:
Consider the dataset given below:
Sky Temperature Humid Wind Water Forest Output
Sunny Warm Normal Strong Warm Same Yes
Sunny Warm High Strong Warm Same Yes
Rainy Cold High Strong Warm Change No
Sunny Warm High Strong Cool Change Yes
23. For instance 4 : <'sunny','warm','high','strong','cool','change’>
and positive output.
G4 = G3
S4 = ['sunny','warm',?,'strong', ?, ?]
At last, by synchronizing the G4 and S4 algorithm produce
the output.
Output :
G = [['sunny', ?, ?, ?, ?, ?], [?, 'warm', ?, ?, ?, ?]]
S = ['sunny','warm',?,'strong', ?, ?]
24. Vapnik-Chervonenkis Dimension
Let us say we have a dataset containing N points.
These N points can be labeled in 2N ways as positive and
negative.
Therefore, 2N different learning problems can be defined by
N data points.
If for any of these problems, we can find a hypothesis h∈H
that separates the positive examples from the negative,
then we say H shatters N points.
That is, any learning problem definable by N examples can
be learned with no error by a hypothesis drawn from H.
25. The maximum number of points that VC dimension can be
shattered by H is called the Vapnik-Chervonenkis (VC)
dimension of H, is denoted as VC(H), and measures the
capacity of H.
26. VC Dimension
N points can be labeled in 2N ways as +/–
H shatters N if there
exists h H consistent
for any of these:
VC(H ) = N
An axis-aligned rectangle shatters 4 points only !
27. How many training examples N should we have, such that with
probability at least 1 ‒ δ, h has error at most ε ?
(Blumer et al., 1989)
Each strip is at most ε/4
Pr that we miss a strip 1‒ ε/4
Pr that N instances miss a strip (1 ‒ ε/4)N
Pr that N instances miss 4 strips 4(1 ‒ ε/4)N
4(1 ‒ ε/4)N ≤ δ and (1 ‒ x)≤exp( ‒ x)
4exp(‒ εN/4) ≤ δ and N ≥ (4/ε)log(4/δ)
Probably Approximately Correct
(PAC) Learning
28. Noise
Noise is any
unwanted
anomaly in the
data and due to
noise, the class
may be more
difficult to learn
and zero error
may be
infeasible with a
simple
hypothesis
class. (see
figure 2.8)
29. There are several interpretations of noise:
There may be imprecision in recording the input attributes, which
may shift the data points in the input space.
There may be errors in labeling the data points, which may relabel
positive instances as negative and vice versa. This is sometimes
called teacher noise.
There may be additional attributes, which we have not taken into
account, that affect the label of an instance. Such attributes may
be hidden or latent in that they may be unobservable. The effect of
these neglected attributes is thus modeled as a random
component and is included in “noise.”
30. As can be seen in figure 2.8, when there is noise, there is
not a simple boundary between the positive and negative
instances and to separate them, one needs a complicated
hypothesis that corresponds to a hypothesis class with
larger capacity.
A rectangle can be defined by four numbers, but to define
a more complicated shape one needs a more complex
model with a much larger number of parameters.
With a complex model one can make a perfect fit to the
data and attain zero error; see the wiggly shape in figure
2.8.
31. 31
Use the simpler one because
Simpler to use
(lower computational
complexity)
Easier to train (lower
space complexity)
Easier to explain
(more interpretable)
Generalizes better (lower
variance - Occam’s razor)
Noise and Model Complexity
32. Learning Multiple Classes
In our example of learning a family car, we have positive
examples belonging to the class family car and the
negative examples belonging to all other cars.
This is a two-class problem.
In the general case, we have K classes denoted as Ci, i =
1, . . . , K, and an input instance belongs to one and
exactly one of them.
33. Multiple Classes, Ci i=1,...,K
N
t
t
t
,r 1
}
{
x
X
,
if
0
if
1
i
j
r
j
t
i
t
t
i
C
C
x
x
Train hypotheses
hi(x), i =1,...,K:
,
if
0
if
1
i
j
h
j
t
i
t
t
i
C
C
x
x
x
34. In machine learning for classification, we would like to
learn the boundary separating the instances of one class
from the instances of all other classes.
Thus we view a K-class classification problem as K two-
class problems.
The training examples belonging to Ci are the positive
instances of hypothesis hi and the examples of all other
classes are the negative instances of hi.
The total empirical error takes a sum over the predictions
for all classes over all instances:
35. Regression
In classification, given an input, the output that is
generated is Boolean; it is a yes/no answer.
When the output is a numeric value, what we would like
to learn is not a class, C(x) ∈ {0, 1}, but is a numeric
function.
In machine learning, the function is not known but we
have a training set of examples drawn from it
36.
37. Regression 0
1 w
x
w
x
g
0
1
2
2 w
x
w
x
w
x
g
N
t
t
t
x
g
r
N
g
E
1
2
1
| X
N
t
t
t
w
x
w
r
N
w
,
w
E
1
2
0
1
0
1
1
| X
t
t
t
N
t
t
t
x
f
r
r
r
x 1
,
X
38. Model Selection & Generalization
Learning is an ill-posed problem; data is not sufficient to
find a unique solution
The need for inductive bias, assumptions about H
Generalization: How well a model performs on new data
Overfitting: H more complex than C or f
Underfitting: H less complex than C or f
39. Triple Trade-Off
There is a trade-off between three factors (Dietterich,
2003):
1. Complexity of H, c (H),
2. Training set size, N,
3. Generalization error, E, on new data
As N, E
As c (H), first E and then E
40. Cross-Validation
To estimate generalization error, we need data unseen
during training. We split the data as
Training set (50%)
Validation set (25%)
Test (publication) set (25%)
Resampling when there is few data
41. Dimensions of a Supervised
Learner
1. Model :
2. Loss function:
3. Optimization procedure:
|
x
g
t
t
t
g
,
r
L
E |
| x
X
X
|
min
arg
E
*