Machine learning models are trained with a certain amount of labeled data and will use it to make predictions on unseen data. Based on this data, machines define a set of rules that they apply to all datasets, helping them provide consistent and accurate results. No need to worry about human error or innate bias.
The document discusses machine learning and provides definitions and concepts. It introduces machine learning, defines what learning is, and discusses why machine learning is studied and its relation to other disciplines like artificial intelligence and data mining. It describes different types of learning like supervised vs unsupervised learning. It also discusses representing and designing machine learning systems, including defining the learning task, choosing the training experience and target function, and selecting a learning algorithm.
The document discusses machine learning and provides context for a lecture on the topic. It defines machine learning as a system that improves its performance on a task through experience. It discusses different types of learning including supervised, unsupervised, reinforcement learning. It also covers representing learning problems, designing learning systems, and choosing a target function to learn.
The document discusses machine learning and provides context for a lecture on the topic. It defines machine learning as a system that improves its performance on a task through experience. It discusses different types of learning like supervised vs unsupervised learning. It also discusses defining the learning task, designing a learning system, and choosing a target function to learn from examples.
Machine learning involves using experience to improve performance on some task. It can be viewed as approximating a target function from training data using a learning algorithm. The target function defines what is to be learned based on a task and performance metric. Training data provides examples of the target function, which the learning algorithm uses to induce a hypothesis function. Different learning problems involve different types of experience, target functions, hypothesis spaces, and learning algorithms. Machine learning has a long history and many applications across various domains involving data.
Machine learning involves using experience to improve performance on some task. It is studied as a way to develop intelligent systems and gain knowledge from data. There are many approaches to machine learning including supervised learning from labeled examples, unsupervised learning of clusters in unlabeled data, and reinforcement learning from a series of rewards and punishments. Learning methods represent functions in different ways like tables, rules, or neural networks, and use algorithms like gradient descent or genetic algorithms to optimize these representations based on examples. Machine learning has a long history and is now widely used in applications involving large datasets.
Machine learning involves using experience to improve performance on some task. It can be viewed as approximating a target function from training data using a learning algorithm. The Least Mean Squares algorithm was discussed as a way to learn linear weights to approximate a target evaluation function for the game of checkers from examples of board positions and estimated values. Different representations, learning algorithms, and evaluation methods were also summarized.
This power point presentation provides an overview of machine learning. It discusses what machine learning is, why machines learn, the problems solved by machine learning like image recognition and language translation. It covers the components of learning like data storage, abstraction, generalization and evaluation. Applications of machine learning like retail, finance, medicine are presented. Different learning models like logical, geometric, probabilistic are explained. Finally, the presentation discusses the design process for a machine learning system like choosing the training experience, target function, its representation and the approximation algorithm.
This document provides an introduction to machine learning, including definitions of machine learning, major categories of machine learning tasks, the relationship between machine learning and data mining, and current and future applications. The major categories of machine learning tasks discussed are classification, regression, clustering, relationship discovery, and reinforcement learning. Current applications mentioned include market basket analysis, personalized recommendations, quality control, and medical diagnosis. The document also discusses supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning as machine learning techniques.
The document discusses machine learning and provides definitions and concepts. It introduces machine learning, defines what learning is, and discusses why machine learning is studied and its relation to other disciplines like artificial intelligence and data mining. It describes different types of learning like supervised vs unsupervised learning. It also discusses representing and designing machine learning systems, including defining the learning task, choosing the training experience and target function, and selecting a learning algorithm.
The document discusses machine learning and provides context for a lecture on the topic. It defines machine learning as a system that improves its performance on a task through experience. It discusses different types of learning including supervised, unsupervised, reinforcement learning. It also covers representing learning problems, designing learning systems, and choosing a target function to learn.
The document discusses machine learning and provides context for a lecture on the topic. It defines machine learning as a system that improves its performance on a task through experience. It discusses different types of learning like supervised vs unsupervised learning. It also discusses defining the learning task, designing a learning system, and choosing a target function to learn from examples.
Machine learning involves using experience to improve performance on some task. It can be viewed as approximating a target function from training data using a learning algorithm. The target function defines what is to be learned based on a task and performance metric. Training data provides examples of the target function, which the learning algorithm uses to induce a hypothesis function. Different learning problems involve different types of experience, target functions, hypothesis spaces, and learning algorithms. Machine learning has a long history and many applications across various domains involving data.
Machine learning involves using experience to improve performance on some task. It is studied as a way to develop intelligent systems and gain knowledge from data. There are many approaches to machine learning including supervised learning from labeled examples, unsupervised learning of clusters in unlabeled data, and reinforcement learning from a series of rewards and punishments. Learning methods represent functions in different ways like tables, rules, or neural networks, and use algorithms like gradient descent or genetic algorithms to optimize these representations based on examples. Machine learning has a long history and is now widely used in applications involving large datasets.
Machine learning involves using experience to improve performance on some task. It can be viewed as approximating a target function from training data using a learning algorithm. The Least Mean Squares algorithm was discussed as a way to learn linear weights to approximate a target evaluation function for the game of checkers from examples of board positions and estimated values. Different representations, learning algorithms, and evaluation methods were also summarized.
This power point presentation provides an overview of machine learning. It discusses what machine learning is, why machines learn, the problems solved by machine learning like image recognition and language translation. It covers the components of learning like data storage, abstraction, generalization and evaluation. Applications of machine learning like retail, finance, medicine are presented. Different learning models like logical, geometric, probabilistic are explained. Finally, the presentation discusses the design process for a machine learning system like choosing the training experience, target function, its representation and the approximation algorithm.
This document provides an introduction to machine learning, including definitions of machine learning, major categories of machine learning tasks, the relationship between machine learning and data mining, and current and future applications. The major categories of machine learning tasks discussed are classification, regression, clustering, relationship discovery, and reinforcement learning. Current applications mentioned include market basket analysis, personalized recommendations, quality control, and medical diagnosis. The document also discusses supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning as machine learning techniques.
Basics of machine learning. Fundamentals of machine learning. These slides are collected from different learning materials and organized into one slide set.
Unit 1 - ML - Introduction to Machine Learning.pptxjawad184956
1. Machine learning involves using algorithms to learn from data and make predictions without being explicitly programmed. It includes supervised learning (classification and regression), unsupervised learning (clustering and association), and reinforcement learning.
2. Learning models can be divided into logical models (using logical expressions), geometric models (using geometry of data), and probabilistic models (using probability). Common algorithms include decision trees, k-nearest neighbors, Naive Bayes, and k-means clustering.
3. The learning process involves data storage, abstraction (creating models), generalization (applying knowledge), and evaluation (measuring performance). Machine learning has applications in areas like retail, finance, science, engineering, and artificial intelligence.
This document provides an overview of the Foundations of Machine Learning (CS725) course for Autumn 2011. It introduces machine learning and discusses applications. It covers different machine learning models including supervised learning (classification and regression), unsupervised learning, semi-supervised learning, and active learning. It also discusses related fields, real-world applications, and tools/resources for the course.
The document discusses machine learning techniques and concepts such as supervised learning, linear discriminant analysis, perceptrons, and more. It begins by defining machine learning and different types including supervised learning. It then covers topics like the brain and neurons, concept learning as search, finding maximally specific hypotheses, version spaces, and the candidate elimination algorithm. It also discusses linear discriminants, perceptrons, and applications of machine learning.
Supervised learning is a fundamental concept in machine learning, where a computer algorithm learns from labeled data to make predictions or decisions. It is a type of machine learning paradigm that involves training a model on a dataset where both the input data and the corresponding desired output (or target) are provided. The goal of supervised learning is to learn a mapping or relationship between inputs and outputs so that the model can make accurate predictions on new, unseen data.v
This slide gives brief overview of supervised, unsupervised and reinforcement learning. Algorithms discussed are Naive Bayes, K nearest neighbour, SVM,decision tree, Markov model.
Difference between regression and classification. difference between supervised and reinforcement, iterative functioning of Markov model and machine learning applications.
This document provides an introduction to machine learning concepts including definitions of machine learning, training and test data, and different machine learning techniques. It defines machine learning as a field that allows machines to learn from data without being explicitly programmed. It describes how training data is used to teach a machine and test data is used to evaluate how well a machine has learned. The document outlines common machine learning techniques including supervised learning techniques like classification and regression as well as unsupervised learning techniques like clustering. It provides examples of different algorithms for each technique.
Lecture 2 - Introduction to Machine Learning, a lecture in subject module Sta...Maninda Edirisooriya
Introduction to Statistical and Machine Learning. Explains basics of ML, fundamental concepts of ML, Statistical Learning and Deep Learning. Recommends the learning sources and techniques of Machine Learning. This was one of the lectures of a full course I taught in University of Moratuwa, Sri Lanka on 2023 second half of the year.
This document provides an introduction to machine learning, including definitions and explanations of key concepts such as learning, machine learning, the motivation for machine learning, the three phases of machine learning (training, validation, application), and different learning techniques including rote learning, inductive learning, and deductive learning. It also discusses symbol-based learning, connectionist learning, artificial neural networks, deep learning, and how machine learning is different from other forms of artificial intelligence.
Machine learning, deep learning, and artificial intelligence are summarized. Machine learning involves using algorithms to learn from data and make predictions without being explicitly programmed. Deep learning uses neural networks with many layers to learn representations of data with multiple levels of abstraction. Artificial intelligence is the broader field of building intelligent machines that can think and act like humans. Supervised, unsupervised, semi-supervised and reinforcement learning techniques are described along with common applications such as classification, clustering, recommendation systems, and game playing.
Introduction AI ML& Mathematicals of ML.pdfGandhiMathy6
Machine learning uses probability theory to deal with uncertainty that arises from noisy data, limited data sets, and ambiguity. Probability theory provides a framework to quantify and manipulate uncertainty. It allows optimal predictions given available information, even if that information is incomplete. Key concepts in probability theory for machine learning include defining sample spaces and events, calculating probabilities, working with joint, conditional, and independent probabilities, and using Bayes' rule. These concepts help machine learning algorithms make inferences from data.
The document discusses different approaches to artificial intelligence, including rule-based and learning-based systems. It describes rule-based systems as using if-then rules to reach conclusions, while learning-based systems can adapt existing knowledge through learning. Machine learning is discussed as a type of learning-based AI that allows systems to learn from data without being explicitly programmed. Deep learning is described as a subset of machine learning that uses neural networks with multiple layers to learn from examples in a way similar to the human brain.
The document discusses different types of machine learning including supervised learning, unsupervised learning, and reinforcement learning. It provides examples of each type, such as using labeled data to classify emails as spam or not spam for supervised learning, grouping fruits by color without labels for unsupervised learning, and using rewards to guide an agent through a maze for reinforcement learning. The document also covers applications of machine learning across different domains like banking, biomedical, computer, and environment.
1. Machine learning is a set of techniques that use data to build models that can make predictions without being explicitly programmed.
2. There are two main types of machine learning: supervised learning, where the model is trained on labeled examples, and unsupervised learning, where the model finds patterns in unlabeled data.
3. Common machine learning algorithms include linear regression, logistic regression, decision trees, support vector machines, naive Bayes, k-nearest neighbors, k-means clustering, and random forests. These can be used for regression, classification, clustering, and dimensionality reduction.
Machine learning is a branch of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" with data, without being explicitly programmed. The goal of machine learning is to build programs that can teach themselves to grow and change when exposed to new data. There are supervised, unsupervised, and reinforcement learning techniques used in machine learning applications across many fields including computer vision, speech recognition, robotics, healthcare, and finance.
This document provides an overview of various machine learning algorithms. It discusses supervised learning algorithms like decision trees, naive Bayes, and support vector machines. Unsupervised learning algorithms covered include k-means clustering and principal component analysis. Semi-supervised, reinforcement, and ensemble learning are also summarized. Neural networks and instance-based learning are described. A wide range of applications of machine learning are listed and the document concludes with future opportunities for machine learning.
Accident detection system project report.pdfKamal Acharya
The Rapid growth of technology and infrastructure has made our lives easier. The
advent of technology has also increased the traffic hazards and the road accidents take place
frequently which causes huge loss of life and property because of the poor emergency facilities.
Many lives could have been saved if emergency service could get accident information and
reach in time. Our project will provide an optimum solution to this draw back. A piezo electric
sensor can be used as a crash or rollover detector of the vehicle during and after a crash. With
signals from a piezo electric sensor, a severe accident can be recognized. According to this
project when a vehicle meets with an accident immediately piezo electric sensor will detect the
signal or if a car rolls over. Then with the help of GSM module and GPS module, the location
will be sent to the emergency contact. Then after conforming the location necessary action will
be taken. If the person meets with a small accident or if there is no serious threat to anyone’s
life, then the alert message can be terminated by the driver by a switch provided in order to
avoid wasting the valuable time of the medical rescue team.
Height and depth gauge linear metrology.pdfq30122000
Height gauges may also be used to measure the height of an object by using the underside of the scriber as the datum. The datum may be permanently fixed or the height gauge may have provision to adjust the scale, this is done by sliding the scale vertically along the body of the height gauge by turning a fine feed screw at the top of the gauge; then with the scriber set to the same level as the base, the scale can be matched to it. This adjustment allows different scribers or probes to be used, as well as adjusting for any errors in a damaged or resharpened probe.
Basics of machine learning. Fundamentals of machine learning. These slides are collected from different learning materials and organized into one slide set.
Unit 1 - ML - Introduction to Machine Learning.pptxjawad184956
1. Machine learning involves using algorithms to learn from data and make predictions without being explicitly programmed. It includes supervised learning (classification and regression), unsupervised learning (clustering and association), and reinforcement learning.
2. Learning models can be divided into logical models (using logical expressions), geometric models (using geometry of data), and probabilistic models (using probability). Common algorithms include decision trees, k-nearest neighbors, Naive Bayes, and k-means clustering.
3. The learning process involves data storage, abstraction (creating models), generalization (applying knowledge), and evaluation (measuring performance). Machine learning has applications in areas like retail, finance, science, engineering, and artificial intelligence.
This document provides an overview of the Foundations of Machine Learning (CS725) course for Autumn 2011. It introduces machine learning and discusses applications. It covers different machine learning models including supervised learning (classification and regression), unsupervised learning, semi-supervised learning, and active learning. It also discusses related fields, real-world applications, and tools/resources for the course.
The document discusses machine learning techniques and concepts such as supervised learning, linear discriminant analysis, perceptrons, and more. It begins by defining machine learning and different types including supervised learning. It then covers topics like the brain and neurons, concept learning as search, finding maximally specific hypotheses, version spaces, and the candidate elimination algorithm. It also discusses linear discriminants, perceptrons, and applications of machine learning.
Supervised learning is a fundamental concept in machine learning, where a computer algorithm learns from labeled data to make predictions or decisions. It is a type of machine learning paradigm that involves training a model on a dataset where both the input data and the corresponding desired output (or target) are provided. The goal of supervised learning is to learn a mapping or relationship between inputs and outputs so that the model can make accurate predictions on new, unseen data.v
This slide gives brief overview of supervised, unsupervised and reinforcement learning. Algorithms discussed are Naive Bayes, K nearest neighbour, SVM,decision tree, Markov model.
Difference between regression and classification. difference between supervised and reinforcement, iterative functioning of Markov model and machine learning applications.
This document provides an introduction to machine learning concepts including definitions of machine learning, training and test data, and different machine learning techniques. It defines machine learning as a field that allows machines to learn from data without being explicitly programmed. It describes how training data is used to teach a machine and test data is used to evaluate how well a machine has learned. The document outlines common machine learning techniques including supervised learning techniques like classification and regression as well as unsupervised learning techniques like clustering. It provides examples of different algorithms for each technique.
Lecture 2 - Introduction to Machine Learning, a lecture in subject module Sta...Maninda Edirisooriya
Introduction to Statistical and Machine Learning. Explains basics of ML, fundamental concepts of ML, Statistical Learning and Deep Learning. Recommends the learning sources and techniques of Machine Learning. This was one of the lectures of a full course I taught in University of Moratuwa, Sri Lanka on 2023 second half of the year.
This document provides an introduction to machine learning, including definitions and explanations of key concepts such as learning, machine learning, the motivation for machine learning, the three phases of machine learning (training, validation, application), and different learning techniques including rote learning, inductive learning, and deductive learning. It also discusses symbol-based learning, connectionist learning, artificial neural networks, deep learning, and how machine learning is different from other forms of artificial intelligence.
Machine learning, deep learning, and artificial intelligence are summarized. Machine learning involves using algorithms to learn from data and make predictions without being explicitly programmed. Deep learning uses neural networks with many layers to learn representations of data with multiple levels of abstraction. Artificial intelligence is the broader field of building intelligent machines that can think and act like humans. Supervised, unsupervised, semi-supervised and reinforcement learning techniques are described along with common applications such as classification, clustering, recommendation systems, and game playing.
Introduction AI ML& Mathematicals of ML.pdfGandhiMathy6
Machine learning uses probability theory to deal with uncertainty that arises from noisy data, limited data sets, and ambiguity. Probability theory provides a framework to quantify and manipulate uncertainty. It allows optimal predictions given available information, even if that information is incomplete. Key concepts in probability theory for machine learning include defining sample spaces and events, calculating probabilities, working with joint, conditional, and independent probabilities, and using Bayes' rule. These concepts help machine learning algorithms make inferences from data.
The document discusses different approaches to artificial intelligence, including rule-based and learning-based systems. It describes rule-based systems as using if-then rules to reach conclusions, while learning-based systems can adapt existing knowledge through learning. Machine learning is discussed as a type of learning-based AI that allows systems to learn from data without being explicitly programmed. Deep learning is described as a subset of machine learning that uses neural networks with multiple layers to learn from examples in a way similar to the human brain.
The document discusses different types of machine learning including supervised learning, unsupervised learning, and reinforcement learning. It provides examples of each type, such as using labeled data to classify emails as spam or not spam for supervised learning, grouping fruits by color without labels for unsupervised learning, and using rewards to guide an agent through a maze for reinforcement learning. The document also covers applications of machine learning across different domains like banking, biomedical, computer, and environment.
1. Machine learning is a set of techniques that use data to build models that can make predictions without being explicitly programmed.
2. There are two main types of machine learning: supervised learning, where the model is trained on labeled examples, and unsupervised learning, where the model finds patterns in unlabeled data.
3. Common machine learning algorithms include linear regression, logistic regression, decision trees, support vector machines, naive Bayes, k-nearest neighbors, k-means clustering, and random forests. These can be used for regression, classification, clustering, and dimensionality reduction.
Machine learning is a branch of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" with data, without being explicitly programmed. The goal of machine learning is to build programs that can teach themselves to grow and change when exposed to new data. There are supervised, unsupervised, and reinforcement learning techniques used in machine learning applications across many fields including computer vision, speech recognition, robotics, healthcare, and finance.
This document provides an overview of various machine learning algorithms. It discusses supervised learning algorithms like decision trees, naive Bayes, and support vector machines. Unsupervised learning algorithms covered include k-means clustering and principal component analysis. Semi-supervised, reinforcement, and ensemble learning are also summarized. Neural networks and instance-based learning are described. A wide range of applications of machine learning are listed and the document concludes with future opportunities for machine learning.
Accident detection system project report.pdfKamal Acharya
The Rapid growth of technology and infrastructure has made our lives easier. The
advent of technology has also increased the traffic hazards and the road accidents take place
frequently which causes huge loss of life and property because of the poor emergency facilities.
Many lives could have been saved if emergency service could get accident information and
reach in time. Our project will provide an optimum solution to this draw back. A piezo electric
sensor can be used as a crash or rollover detector of the vehicle during and after a crash. With
signals from a piezo electric sensor, a severe accident can be recognized. According to this
project when a vehicle meets with an accident immediately piezo electric sensor will detect the
signal or if a car rolls over. Then with the help of GSM module and GPS module, the location
will be sent to the emergency contact. Then after conforming the location necessary action will
be taken. If the person meets with a small accident or if there is no serious threat to anyone’s
life, then the alert message can be terminated by the driver by a switch provided in order to
avoid wasting the valuable time of the medical rescue team.
Height and depth gauge linear metrology.pdfq30122000
Height gauges may also be used to measure the height of an object by using the underside of the scriber as the datum. The datum may be permanently fixed or the height gauge may have provision to adjust the scale, this is done by sliding the scale vertically along the body of the height gauge by turning a fine feed screw at the top of the gauge; then with the scriber set to the same level as the base, the scale can be matched to it. This adjustment allows different scribers or probes to be used, as well as adjusting for any errors in a damaged or resharpened probe.
Impartiality as per ISO /IEC 17025:2017 StandardMuhammadJazib15
This document provides basic guidelines for imparitallity requirement of ISO 17025. It defines in detial how it is met and wiudhwdih jdhsjdhwudjwkdbjwkdddddddddddkkkkkkkkkkkkkkkkkkkkkkkwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwioiiiiiiiiiiiii uwwwwwwwwwwwwwwwwhe wiqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq gbbbbbbbbbbbbb owdjjjjjjjjjjjjjjjjjjjj widhi owqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq uwdhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhwqiiiiiiiiiiiiiiiiiiiiiiiiiiiiw0pooooojjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj whhhhhhhhhhh wheeeeeeee wihieiiiiii wihe
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AI in customer support Use cases solutions development and implementation.pdfmahaffeycheryld
AI in customer support will integrate with emerging technologies such as augmented reality (AR) and virtual reality (VR) to enhance service delivery. AR-enabled smart glasses or VR environments will provide immersive support experiences, allowing customers to visualize solutions, receive step-by-step guidance, and interact with virtual support agents in real-time. These technologies will bridge the gap between physical and digital experiences, offering innovative ways to resolve issues, demonstrate products, and deliver personalized training and support.
https://www.leewayhertz.com/ai-in-customer-support/#How-does-AI-work-in-customer-support
Sri Guru Hargobind Ji - Bandi Chor Guru.pdfBalvir Singh
Sri Guru Hargobind Ji (19 June 1595 - 3 March 1644) is revered as the Sixth Nanak.
• On 25 May 1606 Guru Arjan nominated his son Sri Hargobind Ji as his successor. Shortly
afterwards, Guru Arjan was arrested, tortured and killed by order of the Mogul Emperor
Jahangir.
• Guru Hargobind's succession ceremony took place on 24 June 1606. He was barely
eleven years old when he became 6th Guru.
• As ordered by Guru Arjan Dev Ji, he put on two swords, one indicated his spiritual
authority (PIRI) and the other, his temporal authority (MIRI). He thus for the first time
initiated military tradition in the Sikh faith to resist religious persecution, protect
people’s freedom and independence to practice religion by choice. He transformed
Sikhs to be Saints and Soldier.
• He had a long tenure as Guru, lasting 37 years, 9 months and 3 days
Blood finder application project report (1).pdfKamal Acharya
Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
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.
This presentation is about Food Delivery Systems and how they are developed using the Software Development Life Cycle (SDLC) and other methods. It explains the steps involved in creating a food delivery app, from planning and designing to testing and launching. The slide also covers different tools and technologies used to make these systems work efficiently.
Supermarket Management System Project Report.pdfKamal Acharya
Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.
1. Types of Machine Learning
Machine Learning
Special thanks to F. Hoffmann, T. Mitchell, D. Miller, H. Foundalis, R Mooney
2. Today
• Introduction to machine learning (ML)
– Definitions/theory
– Why important
– How is ML used
• What is learning
– Relation to animal/human learning
• Impact on information science
3. Theories in Information Sciences
• Issues:
– Unified theory? Maybe AI
– Domain of applicability – interactions with the real world
– Conflicts – ML versus human learning
• Theories here are
– Mostly algorithmic
– Some quantitative
• Quality of theories
– Occam’s razor – simplest ML method
– Subsumption of other theories (AI vs ML)
– ML very very popular in real world applications
• ML can be used in nearly every topic involving data that we discuss
• Theories of ML
– Cognitive vs computational
– Mathematical
4. What is Machine Learning?
Aspect of AI: creates knowledge
Definition:
“changes in [a] system that ... enable [it] to do the same
task or tasks drawn from the same population more
efficiently and more effectively the next time.'' (Simon
1983)
There are two ways that a system can improve:
1. By acquiring new knowledge
– acquiring new facts
– acquiring new skills
2. By adapting its behavior
– solving problems more accurately
– solving problems more efficiently
5. 5
What is Learning?
• Herbert Simon: “Learning is any process
by which a system improves performance
from experience.”
• What is the task?
– Classification
– Categorization/clustering
– Problem solving / planning / control
– Prediction
– others
6. 6
Why Study Machine Learning?
Developing Better Computing Systems
• Develop systems that are too difficult/expensive to
construct manually because they require specific
detailed skills or knowledge tuned to a specific task
(knowledge engineering bottleneck).
• Develop systems that can automatically adapt and
customize themselves to individual users.
– Personalized news or mail filter
– Personalized tutoring
• Discover new knowledge from large databases (data
mining).
– Market basket analysis (e.g. diapers and beer)
– Medical text mining (e.g. migraines to calcium channel blockers
to magnesium)
7. 7
Related Disciplines
• Artificial Intelligence
• Data Mining
• Probability and Statistics
• Information theory
• Numerical optimization
• Computational complexity theory
• Control theory (adaptive)
• Psychology (developmental, cognitive)
• Neurobiology
• Linguistics
• Philosophy
8. Human vs machine learning
• Cognitive science vs computational
science
– Animal learning vs machine learning
• Don’t fly like birds
– Many ML models are based on human types
of learning
• Evolution vs machine learning
– Adaptation vs learning
9. Adaptive vs machine learning
• An adaptive system is a set of interacting or interdependent entities,
real or abstract, forming an integrated whole that together are able
to respond to environmental changes or changes in the interacting
parts. Feedback loops represent a key feature of adaptive systems,
allowing the response to changes; examples of adaptive systems
include: natural ecosystems, individual organisms, human
communities, human organizations, and human families.
• Some artificial systems can be adaptive as well; for instance, robots
employ control systems that utilize feedback loops to sense new
conditions in their environment and adapt accordingly.
10. Types of Learning
• Induction vs deduction
• Rote learning (memorization)
• Advice or instructional learning
• Learning by example or practice
– Most popular; many applications
• Learning by analogy; transfer learning
• Discovery learning
• Others?
11. Levels of Learning
Training
Many learning methods involve training
• Training is the acquisition of knowledge, skills, and
competencies as a result of the teaching of vocational or
practical skills and knowledge that relate to specific
useful competencies (wikipedia).
• Training requires scenarios or examples (data)
12. Types of training experience
• Direct or indirect
• With a teacher or without a teacher
• An eternal problem:
– Make the training experience representative
of the performance goal
13. Types of training
• Supervised learning: uses a series of
labelled examples with direct feedback
• Reinforcement learning: indirect feedback,
after many examples
• Unsupervised/clustering learning: no
feedback
• Semisupervised
14. Types of testing
• Evaluate performance by testing on data
NOT used for testing (both should be
randomly sampled)
• Cross validation methods for small data
sets
• The more (relevant) data the better.
15. Testing
• How well the learned system work?
• Generalization
– Performance on unseen or unknown
scenarios or data
– Brittle vs robust performance
20. Usual ML stages
• Hypothesis, data
• Training or learning
• Testing or generalization
21. Why is machine learning
necessary?
• learning is a hallmark of intelligence; many
would argue that a system that cannot learn is
not intelligent.
• without learning, everything is new; a system
that cannot learn is not efficient because it
rederives each solution and repeatedly makes
the same mistakes.
Why is learning possible?
Because there are regularities in the world.
23. Many online software packages &
datasets
• Data sets
• UC Irvine
• http://www.kdnuggets.com/datasets/index.html
• Software (much related to data mining)
• JMIR Open Source
• Weka
• Shogun
• RapidMiner
• ODM
• Orange
• CMU
• Several researchers put their software online
24. Defining the Learning Task
Improve on task, T, with respect to
performance metric, P, based on experience, E.
T: Playing checkers
P: Percentage of games won against an arbitrary opponent
E: Playing practice games against itself
T: Recognizing hand-written words
P: Percentage of words correctly classified
E: Database of human-labeled images of handwritten words
T: Driving on four-lane highways using vision sensors
P: Average distance traveled before a human-judged error
E: A sequence of images and steering commands recorded while
observing a human driver.
T: Categorize email messages as spam or legitimate.
P: Percentage of email messages correctly classified.
E: Database of emails, some with human-given labels
25. Designing a Learning System
• Choose the training experience
• Choose exactly what is too be learned, i.e.
the target function.
• Choose how to represent the target function.
• Choose a learning algorithm to infer the
target function from the experience.
Environment/
Experience
Learner
Knowledge
Performance
Element
26. Sample Learning Problem
• Learn to play checkers from self-play
• Develop an approach analogous to that
used in the first machine learning system
developed by Arthur Samuels at IBM in
1959.
27. Training Experience
• Direct experience: Given sample input and
output pairs for a useful target function.
– Checker boards labeled with the correct move, e.g.
extracted from record of expert play
• Indirect experience: Given feedback which is not
direct I/O pairs for a useful target function.
– Potentially arbitrary sequences of game moves and
their final game results.
• Credit/Blame Assignment Problem: How to
assign credit blame to individual moves given
only indirect feedback?
28. Source of Training Data
• Provided random examples outside of the
learner’s control.
– Negative examples available or only positive?
• Good training examples selected by a
“benevolent teacher.”
– “Near miss” examples
• Learner can query an oracle about class of an
unlabeled example in the environment.
• Learner can construct an arbitrary example and
query an oracle for its label.
• Learner can design and run experiments
directly in the environment without any human
guidance.
29. Training vs. Test Distribution
• Generally assume that the training and
test examples are independently drawn
from the same overall distribution of data.
– IID: Independently and identically distributed
• If examples are not independent, requires
collective classification.
• If test distribution is different, requires
transfer learning.
30. Choosing a Target Function
• What function is to be learned and how will it be
used by the performance system?
• For checkers, assume we are given a function
for generating the legal moves for a given board
position and want to decide the best move.
– Could learn a function:
ChooseMove(board, legal-moves) → best-move
– Or could learn an evaluation function, V(board) →
R, that gives each board position a score for how
favorable it is. V can be used to pick a move by
applying each legal move, scoring the resulting
board position, and choosing the move that results in
the highest scoring board position.
31. Ideal Definition of V(b)
• If b is a final winning board, then V(b) = 100
• If b is a final losing board, then V(b) = –100
• If b is a final draw board, then V(b) = 0
• Otherwise, then V(b) = V(b´), where b´ is the
highest scoring final board position that is
achieved starting from b and playing optimally
until the end of the game (assuming the
opponent plays optimally as well).
– Can be computed using complete mini-max search
of the finite game tree.
32. Approximating V(b)
• Computing V(b) is intractable since it
involves searching the complete
exponential game tree.
• Therefore, this definition is said to be non-
operational.
• An operational definition can be
computed in reasonable (polynomial) time.
• Need to learn an operational
approximation to the ideal evaluation
function.
33. Representing the Target
Function
• Target function can be represented in many
ways: lookup table, symbolic rules, numerical
function, neural network.
• There is a trade-off between the expressiveness
of a representation and the ease of learning.
• The more expressive a representation, the
better it will be at approximating an arbitrary
function; however, the more examples will be
needed to learn an accurate function.
34. Linear Function for
Representing V(b)
• In checkers, use a linear approximation of the
evaluation function.
– bp(b): number of black pieces on board b
– rp(b): number of red pieces on board b
– bk(b): number of black kings on board b
– rk(b): number of red kings on board b
– bt(b): number of black pieces threatened (i.e. which
can be immediately taken by red on its next turn)
– rt(b): number of red pieces threatened
)
(
)
(
)
(
)
(
)
(
)
(
)
( 6
5
4
3
2
1
0 b
rt
w
b
bt
w
b
rk
w
b
bk
w
b
rp
w
b
bp
w
w
b
V
35. Obtaining Training Values
• Direct supervision may be available for the
target function.
– < <bp=3,rp=0,bk=1,rk=0,bt=0,rt=0>, 100>
(win for black)
• With indirect feedback, training values can
be estimated using temporal difference
learning (used in reinforcement learning
where supervision is delayed reward).
36. Temporal Difference Learning
• Estimate training values for intermediate (non-
terminal) board positions by the estimated
value of their successor in an actual game
trace.
where successor(b) is the next board position
where it is the program’s move in actual play.
• Values towards the end of the game are
initially more accurate and continued training
slowly “backs up” accurate values to earlier
board positions.
))
successor(
(
)
( b
V
b
Vtrain
37. Learning Algorithm
• Uses training values for the target function to
induce a hypothesized definition that fits these
examples and hopefully generalizes to
unseen examples.
• In statistics, learning to approximate a
continuous function is called regression.
• Attempts to minimize some measure of error
(loss function) such as mean squared
error:
B
b
V
b
V
E B
b
train
2
)]
(
)
(
[
38. Least Mean Squares (LMS)
Algorithm
• A gradient descent algorithm that
incrementally updates the weights of a linear
function in an attempt to minimize the mean
squared error
Until weights converge :
For each training example b do :
1) Compute the absolute error :
2) For each board feature, fi, update its weight,
wi :
for some small constant (learning rate) c
)
(
)
(
)
( b
V
b
V
b
error train
)
(b
error
f
c
w
w i
i
i
39. LMS Discussion
• Intuitively, LMS executes the following rules:
– If the output for an example is correct, make no
change.
– If the output is too high, lower the weights
proportional to the values of their corresponding
features, so the overall output decreases
– If the output is too low, increase the weights
proportional to the values of their corresponding
features, so the overall output increases.
• Under the proper weak assumptions, LMS can
be proven to eventetually converge to a set of
weights that minimizes the mean squared error.
40. Lessons Learned about
Learning
• Learning can be viewed as using direct or
indirect experience to approximate a chosen
target function.
• Function approximation can be viewed as a
search through a space of hypotheses
(representations of functions) for one that best
fits a set of training data.
• Different learning methods assume different
hypothesis spaces (representation languages)
and/or employ different search techniques.
41. Various Function Representations
• Numerical functions
– Linear regression
– Neural networks
– Support vector machines
• Symbolic functions
– Decision trees
– Rules in propositional logic
– Rules in first-order predicate logic
• Instance-based functions
– Nearest-neighbor
– Case-based
• Probabilistic Graphical Models
– Naïve Bayes
– Bayesian networks
– Hidden-Markov Models (HMMs)
– Probabilistic Context Free Grammars (PCFGs)
– Markov networks
43. Evaluation of Learning Systems
• Experimental
– Conduct controlled cross-validation experiments to
compare various methods on a variety of benchmark
datasets.
– Gather data on their performance, e.g. test accuracy,
training-time, testing-time.
– Analyze differences for statistical significance.
• Theoretical
– Analyze algorithms mathematically and prove
theorems about their:
• Computational complexity
• Ability to fit training data
• Sample complexity (number of training examples needed to
learn an accurate function)
44. History of Machine Learning
• 1950s
– Samuel’s checker player
– Selfridge’s Pandemonium
• 1960s:
– Neural networks: Perceptron
– Pattern recognition
– Learning in the limit theory
– Minsky and Papert prove limitations of Perceptron
• 1970s:
– Symbolic concept induction
– Winston’s arch learner
– Expert systems and the knowledge acquisition bottleneck
– Quinlan’s ID3
– Michalski’s AQ and soybean diagnosis
– Scientific discovery with BACON
– Mathematical discovery with AM
45. History of Machine Learning
(cont.)
• 1980s:
– Advanced decision tree and rule learning
– Explanation-based Learning (EBL)
– Learning and planning and problem solving
– Utility problem
– Analogy
– Cognitive architectures
– Resurgence of neural networks (connectionism, backpropagation)
– Valiant’s PAC Learning Theory
– Focus on experimental methodology
• 1990s
– Data mining
– Adaptive software agents and web applications
– Text learning
– Reinforcement learning (RL)
– Inductive Logic Programming (ILP)
– Ensembles: Bagging, Boosting, and Stacking
– Bayes Net learning
46. History of Machine Learning
(cont.)
• 2000s
– Support vector machines
– Kernel methods
– Graphical models
– Statistical relational learning
– Transfer learning
– Sequence labeling
– Collective classification and structured outputs
– Computer Systems Applications
• Compilers
• Debugging
• Graphics
• Security (intrusion, virus, and worm detection)
– Email management
– Personalized assistants that learn
– Learning in robotics and vision
48. Supervised Learning Classification
• Example: Cancer diagnosis
• Use this training set to learn how to classify patients
where diagnosis is not known:
• The input data is often easily obtained, whereas the
classification is not.
Input Data Classification
Patient ID # of Tumors Avg Area Avg Density Diagnosis
1 5 20 118 Malignant
2 3 15 130 Benign
3 7 10 52 Benign
4 2 30 100 Malignant
Patient ID # of Tumors Avg Area Avg Density Diagnosis
101 4 16 95 ?
102 9 22 125 ?
103 1 14 80 ?
Training
Set
Test Set
49. Classification Problem
• Goal: Use training set + some learning method to
produce a predictive model.
• Use this predictive model to classify new data.
• Sample applications:
Application Input Data Classification
Medical Diagnosis Noninvasive tests Results from invasive
measurements
Optical Character
Recognition
Scanned bitmaps Letter A-Z
Protein Folding Amino acid construction Protein shape (helices,
loops, sheets)
Research Paper
Acceptance
Words in paper title Paper accepted or rejected
52. The revolution in robotics
• Cheap robots!!!
• Cheap sensors
• Moore’s law
53. Robotics and ML
Areas that robots are used:
Industrial robots
Military, government and space robots
Service robots for home, healthcare, laboratory
Why are robots used?
Dangerous tasks or in hazardous environments
Repetitive tasks
High precision tasks or those requiring high quality
Labor savings
Control technologies:
Autonomous (self-controlled), tele-operated (remote
control)
54. Industrial Robots
• Uses for robots in
manufacturing:
– Welding
– Painting
– Cutting
– Dispensing
– Assembly
– Polishing/Finishing
– Material Handling
• Packaging, Palletizing
• Machine loading
55. Industrial Robots
• Uses for robots in
Industry/Manufacturing
– Automotive:
• Video - Welding and handling of fuel tanks
from TV show “How It’s Made” on Discovery
Channel. This is a system I worked on in
2003.
– Packaging:
• Video - Robots in food manufacturing.
62. Medical/Healthcare Applications
DaVinci surgical robot by Intuitive
Surgical.
St. Elizabeth Hospital is one of the local hospitals using this robot. You
can see this robot in person during an open house (website).
Japanese health care assistant suit
(HAL - Hybrid Assistive Limb)
Also… Mind-
controlled wheelchair
using NI LabVIEW
64. ALVINN
Drives 70 mph on a public highway
Predecessor of the Google car
Camera
image
30x32 pixels
as inputs
30 outputs
for steering
30x32 weights
into one out of
four hidden
unit
4 hidden
units
65. Learning vs Adaptation
• ”Modification of a behavioral tendency by expertise.”
(Webster 1984)
• ”A learning machine, broadly defined is any device whose
actions are influenced by past experiences.” (Nilsson 1965)
• ”Any change in a system that allows it to perform better
the second time on repetition of the same task or on another
task drawn from the same population.” (Simon 1983)
• ”An improvement in information processing ability that results
from information processing activity.” (Tanimoto 1990)
67. Disciplines relevant to ML
• Artificial intelligence
• Bayesian methods
• Control theory
• Information theory
• Computational complexity theory
• Philosophy
• Psychology and neurobiology
• Statistics
• Many practical problems in engineering
and business
68. Machine Learning as
• Function approximation (mapping)
– Regression
• Classification
• Categorization (clustering)
• Prediction
• Pattern recognition
69. ML in the real world
• Real World Applications Panel: Machine
Learning and Decision Support
• Google
• Orbitz
• Astronomy
70. Working Applications of ML
• Classification of mortgages
• Predicting portfolio performance
• Electrical power control
• Chemical process control
• Character recognition
• Face recognition
• DNA classification
• Credit card fraud detection
• Cancer cell detection
71. Artificial Life
• GOLEM Project (Nature: Lipson, Pollack 2000)
http://www.demo.cs.brandeis.edu/golem/
• Evolve simple electromechanical locomotion machines
from basic building blocks (bars, acuators, artificial
neurons) in a simulation of the physical world (gravity,
friction).
• The individuals that demonstrate the best locomotion ability
are fabricated through rapid prototyping technology.
72. Issues in Machine Learning
• What algorithms can approximate functions
well and when
– How does the number of training examples
influence accuracy
• Problem representation / feature extraction
• Intention/independent learning
• Integrating learning with systems
• What are the theoretical limits of learnability
• Transfer learning
• Continuous learning
74. Scaling issues in ML
• Number of
– Inputs
– Outputs
– Batch vs realtime
– Training vs testing
75. Machine Learning versus Human
Learning
– Some ML behavior can challenge the performance
of human experts (e.g., playing chess)
– Although ML sometimes matches human learning
capabilities, it is not able to learn as well as
humans or in the same way that humans do
– There is no claim that machine learning can be
applied in a truly creative way
– Formal theories of ML systems exist but are often
lacking (why a method succeeds or fails is not
clear)
– ML success is often attributed to manipulation of
symbols (rather than mere numeric information)
76. Observations
• ML has many practical applications and is
probably the most used method in AI.
• ML is also an active research area
• Role of cognitive science
• Computational model of cognition
• ACT-R
• Role of neuroscience
• Computational model of the brain
• Neural networks
• Brain vs mind; hardware vs software
• Nearly all ML is still dependent on human
“guidance”
77. Questions
• How does ML affect information science?
• Natural vs artificial learning – which is
better?
• Is ML needed in all problems?
• What are the future directions of ML?