Session 21
•Download as PPT, PDF•
0 likes•162 views
The document discusses different types of machine learning including supervised learning, unsupervised learning, and reinforced learning. It provides examples of classification problems and how machine learning algorithms can be used to build models to classify new data based on patterns learned from training data. Examples of learning paradigms like decision trees and artificial neural networks are also mentioned. The key benefits of machine learning like improving performance over time and handling unknown environments are highlighted.
Report
Share
Report
Share
![Learning ,[object Object],[object Object],[object Object],[object Object],[object Object]](https://image.slidesharecdn.com/session-213654/85/Session-21-1-320.jpg)
![Which of these are “learning” ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
ML_ Unit_1_PART_A
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
CSI 5387: Concept Learning Systems / Machine Learning
This document provides information about the CSI 5387: Concept Learning Systems / Machine Learning course taught by Dr. Nathalie Japkowicz. The objectives are to introduce machine learning principles, paradigms, and approaches. Students will read and present research papers, and complete a final project proposing and implementing a novel learning scheme. Course requirements include weekly paper critiques, 1-2 presentations, assignments, a project proposal, report, and presentation.
Ensemble methods
Ensemble methods combine multiple machine learning models to obtain better predictive performance than could be obtained from any of the constituent models alone. The document discusses major families of ensemble methods including bagging, boosting, and voting. It provides examples like random forest, AdaBoost, gradient tree boosting, and XGBoost which build ensembles of decision trees. Ensemble methods help reduce variance and prevent overfitting compared to single models.
Ensemble learning
ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.
An ensemble is itself a supervised learning algorithm, because it can be trained and then used to make predictions. The trained ensemble, therefore, represents a single hypothesis. This hypothesis, however, is not necessarily contained within the hypothesis space of the models from which it is built.
Continuous Adaptation via Meta Learning in Nonstationary and Competitive Envi...
Presentation slides on "Continuous Adaptation via Meta Learning in Nonstationary and Competitive Environments".
Ensemble learning
Ensemble Learning is a technique that creates multiple models and then combines them to produce improved results.
Ensemble learning usually produces more accurate solutions than a single model would.
Machine Learning (CS 567) Lecture 2
The document discusses a machine learning lecture on supervised learning. It provides an overview of administrative details for the class like project requirements and deadlines. It then reviews concepts from the previous lecture like the differences between supervised, unsupervised, and reinforcement learning. The remainder of the document outlines the topics to be covered in the current lecture, including defining supervised learning, discussing hypothesis spaces, and introducing linear threshold algorithms.
DTCII
This document discusses techniques for text mining and natural language processing. It outlines basic steps like sentence splitting, tokenization, part-of-speech tagging, named entity recognition, and syntactic parsing. It provides examples of using these techniques and challenges that can arise, such as ambiguous tokens. Hidden Markov Models are also introduced as a machine learning approach for part-of-speech tagging.
Recommended
ML_ Unit_1_PART_A
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
CSI 5387: Concept Learning Systems / Machine Learning
This document provides information about the CSI 5387: Concept Learning Systems / Machine Learning course taught by Dr. Nathalie Japkowicz. The objectives are to introduce machine learning principles, paradigms, and approaches. Students will read and present research papers, and complete a final project proposing and implementing a novel learning scheme. Course requirements include weekly paper critiques, 1-2 presentations, assignments, a project proposal, report, and presentation.
Ensemble methods
Ensemble methods combine multiple machine learning models to obtain better predictive performance than could be obtained from any of the constituent models alone. The document discusses major families of ensemble methods including bagging, boosting, and voting. It provides examples like random forest, AdaBoost, gradient tree boosting, and XGBoost which build ensembles of decision trees. Ensemble methods help reduce variance and prevent overfitting compared to single models.
Ensemble learning
ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.
An ensemble is itself a supervised learning algorithm, because it can be trained and then used to make predictions. The trained ensemble, therefore, represents a single hypothesis. This hypothesis, however, is not necessarily contained within the hypothesis space of the models from which it is built.
Continuous Adaptation via Meta Learning in Nonstationary and Competitive Envi...
Presentation slides on "Continuous Adaptation via Meta Learning in Nonstationary and Competitive Environments".
Ensemble learning
Ensemble Learning is a technique that creates multiple models and then combines them to produce improved results.
Ensemble learning usually produces more accurate solutions than a single model would.
Machine Learning (CS 567) Lecture 2
The document discusses a machine learning lecture on supervised learning. It provides an overview of administrative details for the class like project requirements and deadlines. It then reviews concepts from the previous lecture like the differences between supervised, unsupervised, and reinforcement learning. The remainder of the document outlines the topics to be covered in the current lecture, including defining supervised learning, discussing hypothesis spaces, and introducing linear threshold algorithms.
DTCII
This document discusses techniques for text mining and natural language processing. It outlines basic steps like sentence splitting, tokenization, part-of-speech tagging, named entity recognition, and syntactic parsing. It provides examples of using these techniques and challenges that can arise, such as ambiguous tokens. Hidden Markov Models are also introduced as a machine learning approach for part-of-speech tagging.
Flexible and efficient Gaussian process models for machine ...
This document presents a dissertation on developing computationally efficient Gaussian process models for machine learning tasks. The author develops several techniques to reduce the training cost of Gaussian processes from O(N3) to O(NM2), where M is much smaller than the number of training points N. This includes a sparse pseudo-input Gaussian process (SPGP) method that uses a set of M "pseudo-inputs" optimized during training. The author also combines local and global approximations in a partially independent training conditional approach. Further, variable noise models and dimensionality reduction are introduced to increase the applicability of Gaussian processes to complex datasets. Empirical results demonstrate the effectiveness of the proposed methods.
PPTX - Research In Educational Technology: Expanding Possibilities
This document summarizes Richard Anderson's research into expanding education through technology. It discusses using video conferencing for distance education between universities. It also explores tutored video instruction, where pre-recorded lectures are shown with a facilitator, and digital study halls, which aim to provide primary education in rural India. The research found that facilitating interaction across remote sites, high video quality, and active student participation were important for successful distance education. Facilitated video instruction was also effective, especially with support for facilitators. These techniques show promise for enhancing education in low-resource environments.
A Machine Learning Toolkit for Power Systems Security Analysis
This document describes a machine learning toolkit called UMLPSE that has been developed to facilitate power systems security analysis and contingency analysis. UMLPSE has a modular structure combining independent power flow and machine learning tool packages. It incorporates a data warehouse to store operating point data and contingency analysis results. The toolkit is applied to assess contingencies on the power grid of Crete, Greece involving 61 buses and 78 lines. It uses various operating points simulated through different connectivity, load, and generation scenarios to train machine learning models to automatically rank contingencies by risk for new operating points.
mailfilter.ppt
The document discusses techniques for classifying and filtering email messages. It describes using a Naive Bayes classifier with word frequency statistics to efficiently classify messages into folders. The key is leveraging each user's personal filtering preferences through supervised training on their labeled messages. The system was tested on over 7,000 messages across four users, achieving 89% average accuracy at filtering speed of 259 messages per second.
ppt
The document discusses using k-way interaction loglinear models to analyze gene interaction from microarray data. It begins with background on challenges with existing clustering and association rule methods. It then proposes using loglinear models to identify interactions that cannot be explained by pairwise associations alone. The method transforms expression data, finds frequent gene sets, and fits k-way interaction models to identify statistically significant higher-order interactions. Experimental results on yeast data identified known and potentially new biological interactions.
Specializing in Small Businesses and Individuals
Recharge Web Design offers web design services for small businesses and individuals located in Altoona, Pennsylvania. The company was founded in 2003 and aims to provide an affordable, customizable solution for each client. Services include web design, consultation, and project management. Rates are $35 per hour for design, creation, maintenance and revision. Additional fees include domain registration, site hosting, and search engine submission.
3_learning.ppt
The document discusses concepts related to supervised machine learning and decision tree algorithms. It defines key terms like supervised vs unsupervised learning, concept learning, inductive bias, and information gain. It also describes the basic process for learning decision trees, including selecting the best attribute at each node using information gain to create a small tree that correctly classifies examples, and evaluating performance on test data. Extensions like handling real-valued, missing and noisy data, generating rules from trees, and pruning trees to avoid overfitting are also covered.
Induction and Decision Tree Learning (Part 1)
The document discusses machine learning and inductive learning. It provides an overview of types of machine learning including supervised, unsupervised, and reinforcement learning. It also discusses the history of machine learning and how inductive learning works, with the goal being to construct a hypothesis h that approximates the target function f based on examples in the training data. Decision tree learning is introduced as a method for inductive learning.
Machine Learning: Decision Trees Chapter 18.1-18.3
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.
Generalization abstraction
Course: Intro to Computer Science (Malmö Högskola):
knowledge representation and abstraction, decision making, generalization, data acquistion (abstraction), machine learning, similarity
another version of abstraction
Machine Learning and Inductive Inference
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 and Neural Networks
The document discusses various machine learning techniques including k-nearest neighbors, which classifies new data based on its similarity to existing examples; Naive Bayes classifiers, which use Bayes' theorem to classify items based on the presence or absence of features; and decision trees, which classify items by sorting them based on the results of tests on their features and dividing them into branches. Reinforcement learning is also covered, where an agent learns through trial-and-error interactions with an environment by receiving rewards or penalties for actions.
nnml.ppt
This document provides an overview of machine learning and neural network techniques. It defines machine learning as the field that focuses on algorithms that can learn. The document discusses several key components of a machine learning model, including what is being learned (the domain) and from what information the learner is learning. It then summarizes several common machine learning algorithms like k-NN, Naive Bayes classifiers, decision trees, reinforcement learning, and the Rocchio algorithm for relevance feedback in information retrieval. For each technique, it provides a brief definition and examples of applications.
Genetic Algorithms.ppt
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.
Chapter 6 - Learning data and analytics course
The document discusses machine learning and concept learning. It introduces concept learning as learning a function that maps examples into categories. An example of concept learning is classifying mushrooms as poisonous or not based on their attributes. The key aspects of concept learning covered are:
- Representing hypotheses as conjunctions of attributes and values
- Defining a general to specific ordering of hypotheses
- Searching the hypothesis space using an algorithm that starts with the most specific hypothesis and generalizes it when it fails to cover positive examples
The goal is to find the maximally specific hypothesis that is consistent with all training examples.
ppt
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.
Introduction to Machine Learning.
The document discusses machine learning and provides an overview of the field. It defines machine learning, explains why it is useful, and gives a brief tour of different machine learning techniques including decision trees, neural networks, Bayesian networks, and more. It also discusses some issues in machine learning like how learning problem characteristics and algorithms influence accuracy.
nncollovcapaldo2013-131220052427-phpapp01.pdf
The document provides an introduction to machine learning and neural networks. It defines machine learning as a field that allows computers to learn without being explicitly programmed. It also discusses different machine learning algorithms like supervised learning, unsupervised learning, and reinforcement learning. The document then describes neural networks and their biological inspiration from the human brain. It explains the basic structure and functioning of artificial neurons and neural networks. Finally, it discusses common neural network training techniques like backpropagation that are used to minimize errors and update weights in multi-layer neural networks.
nncollovcapaldo2013-131220052427-phpapp01.pdf
This document provides an introduction to machine learning and neural networks. It defines machine learning as a field that allows computers to learn without being explicitly programmed. It also describes the main types of machine learning as supervised learning, unsupervised learning, and reinforcement learning. The document then discusses neural networks and their biological inspiration from the human brain. It provides examples of neural network applications and describes the basic structure and functioning of neural networks.
Machine Learning and Artificial Neural Networks.ppt
Machine learning and neural networks are discussed. Machine learning investigates how knowledge is acquired through experience. A machine learning model includes what is learned (the domain), who is learning (the computer program), and the information source. Techniques discussed include k-nearest neighbors algorithm, Winnow algorithm, naive Bayes classifier, decision trees, and reinforcement learning. Reinforcement learning involves an agent interacting with an environment to optimize outcomes through trial and error.
ML-DecisionTrees.ppt
Machine learning is the process of using algorithms to analyze data and learn from it. The document discusses several machine learning concepts including supervised and unsupervised learning, decision trees, and inductive learning. It provides examples of using decision trees to classify restaurant customers as waiting or leaving based on attributes like reservation status and restaurant fullness. Key algorithms like ID3 use information gain to build decision trees from training data in a top-down greedy manner.
ML-DecisionTrees.ppt
This document provides an overview of machine learning concepts including supervised and unsupervised learning, decision trees, and computational learning theory. It discusses what machine learning is, why we learn, major machine learning paradigms like induction and clustering, and the inductive learning problem. Key algorithms like ID3 for learning decision trees are described. Information theory concepts such as entropy, information gain, and the Huffman coding algorithm are also covered as they relate to evaluating and selecting attributes for decision tree learning.
More Related Content
Viewers also liked
Flexible and efficient Gaussian process models for machine ...
This document presents a dissertation on developing computationally efficient Gaussian process models for machine learning tasks. The author develops several techniques to reduce the training cost of Gaussian processes from O(N3) to O(NM2), where M is much smaller than the number of training points N. This includes a sparse pseudo-input Gaussian process (SPGP) method that uses a set of M "pseudo-inputs" optimized during training. The author also combines local and global approximations in a partially independent training conditional approach. Further, variable noise models and dimensionality reduction are introduced to increase the applicability of Gaussian processes to complex datasets. Empirical results demonstrate the effectiveness of the proposed methods.
PPTX - Research In Educational Technology: Expanding Possibilities
This document summarizes Richard Anderson's research into expanding education through technology. It discusses using video conferencing for distance education between universities. It also explores tutored video instruction, where pre-recorded lectures are shown with a facilitator, and digital study halls, which aim to provide primary education in rural India. The research found that facilitating interaction across remote sites, high video quality, and active student participation were important for successful distance education. Facilitated video instruction was also effective, especially with support for facilitators. These techniques show promise for enhancing education in low-resource environments.
A Machine Learning Toolkit for Power Systems Security Analysis
This document describes a machine learning toolkit called UMLPSE that has been developed to facilitate power systems security analysis and contingency analysis. UMLPSE has a modular structure combining independent power flow and machine learning tool packages. It incorporates a data warehouse to store operating point data and contingency analysis results. The toolkit is applied to assess contingencies on the power grid of Crete, Greece involving 61 buses and 78 lines. It uses various operating points simulated through different connectivity, load, and generation scenarios to train machine learning models to automatically rank contingencies by risk for new operating points.
mailfilter.ppt
The document discusses techniques for classifying and filtering email messages. It describes using a Naive Bayes classifier with word frequency statistics to efficiently classify messages into folders. The key is leveraging each user's personal filtering preferences through supervised training on their labeled messages. The system was tested on over 7,000 messages across four users, achieving 89% average accuracy at filtering speed of 259 messages per second.
ppt
The document discusses using k-way interaction loglinear models to analyze gene interaction from microarray data. It begins with background on challenges with existing clustering and association rule methods. It then proposes using loglinear models to identify interactions that cannot be explained by pairwise associations alone. The method transforms expression data, finds frequent gene sets, and fits k-way interaction models to identify statistically significant higher-order interactions. Experimental results on yeast data identified known and potentially new biological interactions.
Specializing in Small Businesses and Individuals
Recharge Web Design offers web design services for small businesses and individuals located in Altoona, Pennsylvania. The company was founded in 2003 and aims to provide an affordable, customizable solution for each client. Services include web design, consultation, and project management. Rates are $35 per hour for design, creation, maintenance and revision. Additional fees include domain registration, site hosting, and search engine submission.
Viewers also liked (6)
Flexible and efficient Gaussian process models for machine ...
Flexible and efficient Gaussian process models for machine ...
PPTX - Research In Educational Technology: Expanding Possibilities
PPTX - Research In Educational Technology: Expanding Possibilities
A Machine Learning Toolkit for Power Systems Security Analysis
A Machine Learning Toolkit for Power Systems Security Analysis
Similar to Session 21
3_learning.ppt
The document discusses concepts related to supervised machine learning and decision tree algorithms. It defines key terms like supervised vs unsupervised learning, concept learning, inductive bias, and information gain. It also describes the basic process for learning decision trees, including selecting the best attribute at each node using information gain to create a small tree that correctly classifies examples, and evaluating performance on test data. Extensions like handling real-valued, missing and noisy data, generating rules from trees, and pruning trees to avoid overfitting are also covered.
Induction and Decision Tree Learning (Part 1)
The document discusses machine learning and inductive learning. It provides an overview of types of machine learning including supervised, unsupervised, and reinforcement learning. It also discusses the history of machine learning and how inductive learning works, with the goal being to construct a hypothesis h that approximates the target function f based on examples in the training data. Decision tree learning is introduced as a method for inductive learning.
Machine Learning: Decision Trees Chapter 18.1-18.3
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.
Generalization abstraction
Course: Intro to Computer Science (Malmö Högskola):
knowledge representation and abstraction, decision making, generalization, data acquistion (abstraction), machine learning, similarity
another version of abstraction
Machine Learning and Inductive Inference
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 and Neural Networks
The document discusses various machine learning techniques including k-nearest neighbors, which classifies new data based on its similarity to existing examples; Naive Bayes classifiers, which use Bayes' theorem to classify items based on the presence or absence of features; and decision trees, which classify items by sorting them based on the results of tests on their features and dividing them into branches. Reinforcement learning is also covered, where an agent learns through trial-and-error interactions with an environment by receiving rewards or penalties for actions.
nnml.ppt
This document provides an overview of machine learning and neural network techniques. It defines machine learning as the field that focuses on algorithms that can learn. The document discusses several key components of a machine learning model, including what is being learned (the domain) and from what information the learner is learning. It then summarizes several common machine learning algorithms like k-NN, Naive Bayes classifiers, decision trees, reinforcement learning, and the Rocchio algorithm for relevance feedback in information retrieval. For each technique, it provides a brief definition and examples of applications.
Genetic Algorithms.ppt
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.
Chapter 6 - Learning data and analytics course
The document discusses machine learning and concept learning. It introduces concept learning as learning a function that maps examples into categories. An example of concept learning is classifying mushrooms as poisonous or not based on their attributes. The key aspects of concept learning covered are:
- Representing hypotheses as conjunctions of attributes and values
- Defining a general to specific ordering of hypotheses
- Searching the hypothesis space using an algorithm that starts with the most specific hypothesis and generalizes it when it fails to cover positive examples
The goal is to find the maximally specific hypothesis that is consistent with all training examples.
ppt
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.
Introduction to Machine Learning.
The document discusses machine learning and provides an overview of the field. It defines machine learning, explains why it is useful, and gives a brief tour of different machine learning techniques including decision trees, neural networks, Bayesian networks, and more. It also discusses some issues in machine learning like how learning problem characteristics and algorithms influence accuracy.
nncollovcapaldo2013-131220052427-phpapp01.pdf
The document provides an introduction to machine learning and neural networks. It defines machine learning as a field that allows computers to learn without being explicitly programmed. It also discusses different machine learning algorithms like supervised learning, unsupervised learning, and reinforcement learning. The document then describes neural networks and their biological inspiration from the human brain. It explains the basic structure and functioning of artificial neurons and neural networks. Finally, it discusses common neural network training techniques like backpropagation that are used to minimize errors and update weights in multi-layer neural networks.
nncollovcapaldo2013-131220052427-phpapp01.pdf
This document provides an introduction to machine learning and neural networks. It defines machine learning as a field that allows computers to learn without being explicitly programmed. It also describes the main types of machine learning as supervised learning, unsupervised learning, and reinforcement learning. The document then discusses neural networks and their biological inspiration from the human brain. It provides examples of neural network applications and describes the basic structure and functioning of neural networks.
Machine Learning and Artificial Neural Networks.ppt
Machine learning and neural networks are discussed. Machine learning investigates how knowledge is acquired through experience. A machine learning model includes what is learned (the domain), who is learning (the computer program), and the information source. Techniques discussed include k-nearest neighbors algorithm, Winnow algorithm, naive Bayes classifier, decision trees, and reinforcement learning. Reinforcement learning involves an agent interacting with an environment to optimize outcomes through trial and error.
ML-DecisionTrees.ppt
Machine learning is the process of using algorithms to analyze data and learn from it. The document discusses several machine learning concepts including supervised and unsupervised learning, decision trees, and inductive learning. It provides examples of using decision trees to classify restaurant customers as waiting or leaving based on attributes like reservation status and restaurant fullness. Key algorithms like ID3 use information gain to build decision trees from training data in a top-down greedy manner.
ML-DecisionTrees.ppt
This document provides an overview of machine learning concepts including supervised and unsupervised learning, decision trees, and computational learning theory. It discusses what machine learning is, why we learn, major machine learning paradigms like induction and clustering, and the inductive learning problem. Key algorithms like ID3 for learning decision trees are described. Information theory concepts such as entropy, information gain, and the Huffman coding algorithm are also covered as they relate to evaluating and selecting attributes for decision tree learning.
ML-DecisionTrees.ppt
This document provides an overview of machine learning concepts including supervised and unsupervised learning, decision trees, and computational learning theory. It discusses what machine learning is, why we learn, major machine learning paradigms like induction and clustering. It also covers the inductive learning problem, decision tree learning with the ID3 algorithm, and uses information theory concepts like entropy and information gain to select the best attributes to split data. An example decision tree is constructed for a restaurant domain to classify whether a patron will wait or leave.
Introduction
This document provides an overview of machine learning concepts including:
- Machine learning uses data and past experiences to improve future performance on tasks. Learning is guided by minimizing loss or maximizing gain.
- The machine learning process involves data collection, representation, modeling, estimation, and model selection. Representation of input data is important for solving problems.
- Types of learning problems include supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), and reinforcement learning.
- Generalization to new data is important but challenging due to the bias-variance tradeoff. Models can underfit or overfit training data. Appropriate model complexity and regularization are important.
Introduction
This document provides an overview of machine learning concepts including:
- Machine learning uses data and past experiences to improve future performance on tasks. Learning is guided by minimizing loss or maximizing gain.
- The machine learning process involves data collection, representation, modeling, estimation, and model selection. Representation of input data is important for solving problems.
- Types of learning problems include supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), and reinforcement learning.
- Generalization to new data is important but challenging due to the bias-variance tradeoff. Models can underfit or overfit training data. Regularization and more data help address overfitting.
Introduction
This document provides an overview of machine learning concepts including:
- Machine learning uses data and past experiences to improve future performance on tasks. Learning is guided by minimizing loss or maximizing gain.
- The machine learning process involves data collection, representation, modeling, estimation, and model selection. Representation of input data is important for solving problems.
- Types of learning problems include supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), and reinforcement learning.
- Generalization to new data is important but challenging due to the bias-variance tradeoff. Models can underfit or overfit training data. Regularization and more data help address overfitting.
Similar to Session 21 (20)
Machine Learning: Decision Trees Chapter 18.1-18.3
Machine Learning: Decision Trees Chapter 18.1-18.3
Machine Learning and Artificial Neural Networks.ppt
Machine Learning and Artificial Neural Networks.ppt
More from butest
EL MODELO DE NEGOCIO DE YOUTUBE
Este documento analiza el modelo de negocio de YouTube. Explica que YouTube y otros sitios de video online representan un nuevo modelo de negocio para contenidos audiovisuales debido al cambio en los hábitos de consumo causado por las nuevas tecnologías. Describe cómo YouTube aprovecha la participación de los usuarios para mejorar continuamente y atraer una audiencia diferente a la de los medios tradicionales.
LESSONS FROM THE MICHAEL JACKSON TRIAL
The defense was successful in portraying Michael Jackson favorably to the jury in several ways:
1) They dressed Jackson in ornate costumes that conveyed images of purity, innocence, and humility.
2) Jackson was shown entering the courtroom as if on a red carpet, emphasizing his celebrity status.
3) Jackson appeared vulnerable, childlike, and in declining health during the trial, eliciting sympathy from jurors.
4) Defense attorney Tom Mesereau effectively presented a coherent narrative of Jackson as a victim and portrayed Neverland as a place of refuge, undermining the prosecution's arguments.
Timeline: The Life of Michael Jackson
Michael Jackson was born in 1958 in Gary, Indiana and rose to fame in the 1960s as the lead singer of The Jackson 5, topping music charts in the 1970s. As a solo artist in the 1980s, his album Thriller broke music records. In the 1990s and 2000s, Jackson faced several legal issues related to child abuse allegations while continuing to release music. He married Lisa Marie Presley and Debbie Rowe and had two children before his death in 2009.
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
This document appears to be a list of popular books from various authors. It includes over 150 book titles across many genres such as fiction, non-fiction, memoirs, and novels. The books cover a wide range of topics from politics to cooking to autobiographies.
LESSONS FROM THE MICHAEL JACKSON TRIAL
The prosecution lost the Michael Jackson trial due to several key mistakes and weaknesses in their case:
1) The lead prosecutor, Thomas Sneddon, was too personally invested in the case against Jackson, having pursued him for over a decade without success.
2) Sneddon's opening statement was disorganized and weak, failing to effectively outline the prosecution's case.
3) The accuser's mother was not credible and damaged the prosecution's case through her erratic testimony, history of lies and con artist behavior.
4) Many prosecution witnesses were not credible due to prior lawsuits against Jackson, debts owed to him, or having been fired by him. Several witnesses even took the Fifth Amendment.
Com 380, Summer II
Here are three examples of public relations from around the world:
1. The UK government's "Be Clear on Cancer" campaign which aims to raise awareness of cancer symptoms and encourage early diagnosis.
2. Samsung's global brand marketing and sponsorship activities which aim to increase brand awareness and favorability of Samsung products worldwide.
3. The Brazilian government's efforts to improve its international image and relations with other countries through strategic communication and diplomacy.
The three most important functions of public relations are:
1. Media relations because the media is how most organizations reach their key audiences. Strong media relationships are crucial.
2. Writing, because written communication is at the core of public relations and how most information is
PPT
Michael Jackson Please Wait... provides biographical information about Michael Jackson including his birthdate, birthplace, parents, height, interests, idols, favorite foods, films, and more. It discusses his background, career highlights including influential albums like Thriller, and films he appeared in such as The Wiz and Moonwalker. The document contains photos and details about Jackson's life and illustrious music career.
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
The document discusses the process of manufacturing celebrity and its negative byproducts. It argues that celebrities are rarely the best in their individual pursuits like singing, dancing, etc. but become famous due to being products of a system controlled by wealthy elites. This system stifles opportunities for worthy artists and creates feudalism. The document also asserts that manufactured celebrities should not be viewed as role models due to behaviors like drug abuse and narcissism that result from the celebrity-making process.
MICHAEL JACKSON.doc
Michael Jackson was a child star who rose to fame with the Jackson 5 in the late 1960s and early 1970s. As a solo artist in the 1970s and 1980s, he had immense commercial success with albums like Off the Wall, Thriller, and Bad, which featured hit singles and groundbreaking music videos. However, his career and public image were plagued by controversies related to allegations of child sexual abuse in the 1990s and 2000s. He continued recording and performing but faced ongoing media scrutiny into his private life until his death in 2009.
Social Networks: Twitter Facebook SL - Slide 1
The document discusses using social networking tools like Twitter and Facebook in K-12 education. Twitter allows students and teachers to share short updates and can be used to give parents a window into classroom activities. Facebook allows targeted advertising that could be used to promote educational activities. Both tools could help facilitate communication between schools and communities if used properly while managing privacy and security concerns.
Facebook
Facebook has over 300 million active users who log on daily, and allows brands to create public profile pages to interact with users. Pages are for brands and organizations only, while groups can be made by any user about any topic. Pages do not show admin names and have no limits on fans, while groups display admin names and are limited to 5,000 members. Content on pages should aim to provoke action from subscribers and establish a regular posting schedule using a conversational tone.
Executive Summary Hare Chevrolet is a General Motors dealership ...
Hare Chevrolet is a car dealership located in Noblesville, Indiana that has successfully used social media platforms like Twitter, Facebook, and YouTube to create a positive brand image. They invest significant time interacting directly with customers online to foster a sense of community rather than overtly advertising. As a result, Hare Chevrolet has built a large, engaged audience on social media and serves as a model for how brands can use online presences strategically.
Welcome to the Dougherty County Public Library's Facebook and ...
This document provides instructions for signing up for Facebook and Twitter accounts. It outlines the sign up process for both platforms, including filling out forms with name, email, password and other details. It describes how the platforms will then search for friends and suggest people to connect with. It also explains how to search for and follow the Dougherty County Public Library page on both Facebook and Twitter once signed up. The document concludes by thanking participants and providing a contact for any additional questions.
NEWS ANNOUNCEMENT
Paragon Software announces the release of Paragon NTFS for Mac OS X 8.0, which provides full read and write access to NTFS partitions on Macs. It is the fastest NTFS driver on the market, achieving speeds comparable to native Mac file systems. Paragon NTFS for Mac 8.0 fully supports the latest Mac OS X Snow Leopard operating system in 64-bit mode and allows easy transfer of files between Windows and Mac partitions without additional hardware or software.
C-2100 Ultra Zoom.doc
This document provides compatibility information for Olympus digital products used with Macintosh OS X. It lists various digital cameras, photo printers, voice recorders, and accessories along with their connection type and any notes on compatibility. Some products require booting into OS 9.1 for software compatibility or do not support devices that need a serial port. Drivers and software are available for download from Olympus and other websites for many products to enable use with OS X.
MAC Printing on ITS Printers.doc.doc
To use printers managed by the university's Information Technology Services (ITS), students and faculty must install the ITS Remote Printing software on their Mac OS X computer. This allows them to add network printers, log in with their ITS account credentials, and print documents while being charged per page to funds in their pre-paid ITS account. The document provides step-by-step instructions for installing the software, adding a network printer, and printing to that printer from any internet connection on or off campus. It also explains the pay-in-advance printing payment system and how to check printing charges.
Mac OS X Guide.doc
The document provides an overview of the Mac OS X user interface for beginners, including descriptions of the desktop, login screen, desktop elements like the dock and hard disk, and how to perform common tasks like opening files and folders. It also addresses frequently asked questions for Windows users switching to Mac OS X, such as where documents are stored, how to save or find documents, and what the equivalent of the C: drive is in Mac OS X. The document concludes with sections on file management tasks like creating and deleting folders, organizing files within applications, using Spotlight search, and an overview of the Dashboard feature.
hier
This document provides a checklist for securing Mac OS X version 10.5, focusing on hardening the operating system, securing user accounts and administrator accounts, enabling file encryption and permissions, implementing intrusion detection, and maintaining password security. It describes the Unix infrastructure and security framework that Mac OS X is built on, leveraging open source software and following the Common Data Security Architecture model. The checklist can be used to audit a system or harden it against security threats.
WEB DESIGN!
This document summarizes a course on web design that was piloted in the summer of 2003. The course was a 3 credit course that met 4 times a week for lectures and labs. It covered topics such as XHTML, CSS, JavaScript, Photoshop, and building a basic website. 18 students from various majors enrolled. Student and instructor evaluations found the course to be very successful overall, though some improvements were suggested like ensuring proper software and pairing programming/non-programming students. The document also discusses implications of incorporating web design material into existing computer science curriculums.
More from butest (20)
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
Executive Summary Hare Chevrolet is a General Motors dealership ...
Executive Summary Hare Chevrolet is a General Motors dealership ...
Welcome to the Dougherty County Public Library's Facebook and ...
Welcome to the Dougherty County Public Library's Facebook and ...
Session 21
- 9. Classification example Weight Height o x x x x x x x x x x x o o o o o o o o o x o o x x x - weight-lifters o - ballet dancers Features: height, weight
- 10. Classification example - Simple Model Weight Height o x x x x x x x x x x x o o o o o o o o o x o o x x x - weight-lifters o - ballet dancers Decision boundary Features: height, weight
- 11. Classification example - Complex model Weight Height o x x x x x x x x x x x o o o o o o o o o x o o x x x - weight-lifters o - ballet dancers Complex Decision boundary Features: height, weight Note: A simple decision boundary is better than a complex one - It GENERALIZES better.
- 12. Classification example Model Test set Train set Learning system New data Loan Yes/No