Machine learning
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This presentation provides an overview of machine learning. It defines machine learning as a branch of artificial intelligence concerned with developing algorithms that allow computers to learn from empirical data. The presentation discusses different types of machine learning including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. It also outlines common machine learning algorithms and frameworks and explains the core components of representation, evaluation, and optimization used in machine learning algorithms.
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Machine learning seeks to build computer systems that can improve automatically through experience. It involves developing algorithms and techniques that allow computers to "learn" by acquiring knowledge from data without being explicitly programmed. There are two main types of learning - inductive learning, which reasons from examples to reach general conclusions, and deductive learning, where conclusions are logically required by previous statements. Machine learning has many applications including natural language processing, medical diagnosis, and computer vision.
This is a heavily data-oriented
Thomas G. Dietterich discusses three scenarios where standard software engineering methods fail but machine learning methods can be applied successfully. The scenarios are reading checks, testing VLSI wafers, and allocating a camera for a mobile robot. He then outlines fundamental questions in machine learning like incorporating prior knowledge and the tradeoff between accuracy, sample size, and hypothesis complexity. Statistical thinking is needed in computer science due to the data explosion from sources like NASA, Google, and digital images.
Machine Can Think
This document provides an overview of machine learning. It begins with an introduction and definitions, explaining that machine learning allows computers to learn without being explicitly programmed by exploring algorithms that can learn from data. The document then discusses the different types of machine learning problems including supervised learning, unsupervised learning, and reinforcement learning. It provides examples and applications of each type. The document also covers popular machine learning techniques like decision trees, artificial neural networks, and frameworks/tools used for machine learning.
introducción a Machine Learning
This document discusses machine learning concepts including what learning is, different types of learning tasks like classification and problem solving/planning, measuring performance, reasons to study machine learning, related disciplines, defining learning tasks, designing learning systems, sample learning problems, and lessons learned about learning. It uses the example of learning to play checkers to illustrate many of these concepts such as representing the target function, obtaining training data, choosing a learning algorithm, and discussing specific algorithms like least mean squares regression.
2.17Mb ppt
The document discusses machine learning, defining it as using algorithms to automatically learn from labeled examples to create hypotheses that can predict labels for new examples. It provides examples of machine learning applications like spam filtering and autonomous vehicles, and covers different types of learning algorithms like decision trees and neural networks that are used to perform these tasks. The document also discusses why machine learning is useful and relevant disciplines like statistics, psychology, and computer science that contribute to its development.
Machine Learning SPPU Unit 1
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.
Machine learning
Machine learning is a subset of artificial intelligence that allows computers to learn without being explicitly programmed by improving their performance on tasks based on experience. It involves developing algorithms that can learn from and make predictions on data. There are many machine learning algorithms that differ in their representation, evaluation, and optimization methods, and algorithms can perform supervised learning (classification and regression), unsupervised learning (clustering and dimensionality reduction), semi-supervised learning, and reinforcement learning. Machine learning has applications in areas like web search, finance, e-commerce, robotics, and healthcare.
Intro/Overview on Machine Learning Presentation
This document provides an overview of a presentation on machine learning given at Gurukul Kangri University in 2017. It defines machine learning as a field that allows computers to learn without being explicitly programmed. It discusses different machine learning algorithms including supervised learning, unsupervised learning, and semi-supervised learning. Examples of applications of machine learning discussed include data mining, natural language processing, image recognition, and expert systems. The document also contrasts artificial intelligence, machine learning, and deep learning.
Recommended
source1
Machine learning seeks to build computer systems that can improve automatically through experience. It involves developing algorithms and techniques that allow computers to "learn" by acquiring knowledge from data without being explicitly programmed. There are two main types of learning - inductive learning, which reasons from examples to reach general conclusions, and deductive learning, where conclusions are logically required by previous statements. Machine learning has many applications including natural language processing, medical diagnosis, and computer vision.
This is a heavily data-oriented
Thomas G. Dietterich discusses three scenarios where standard software engineering methods fail but machine learning methods can be applied successfully. The scenarios are reading checks, testing VLSI wafers, and allocating a camera for a mobile robot. He then outlines fundamental questions in machine learning like incorporating prior knowledge and the tradeoff between accuracy, sample size, and hypothesis complexity. Statistical thinking is needed in computer science due to the data explosion from sources like NASA, Google, and digital images.
Machine Can Think
This document provides an overview of machine learning. It begins with an introduction and definitions, explaining that machine learning allows computers to learn without being explicitly programmed by exploring algorithms that can learn from data. The document then discusses the different types of machine learning problems including supervised learning, unsupervised learning, and reinforcement learning. It provides examples and applications of each type. The document also covers popular machine learning techniques like decision trees, artificial neural networks, and frameworks/tools used for machine learning.
introducción a Machine Learning
This document discusses machine learning concepts including what learning is, different types of learning tasks like classification and problem solving/planning, measuring performance, reasons to study machine learning, related disciplines, defining learning tasks, designing learning systems, sample learning problems, and lessons learned about learning. It uses the example of learning to play checkers to illustrate many of these concepts such as representing the target function, obtaining training data, choosing a learning algorithm, and discussing specific algorithms like least mean squares regression.
2.17Mb ppt
The document discusses machine learning, defining it as using algorithms to automatically learn from labeled examples to create hypotheses that can predict labels for new examples. It provides examples of machine learning applications like spam filtering and autonomous vehicles, and covers different types of learning algorithms like decision trees and neural networks that are used to perform these tasks. The document also discusses why machine learning is useful and relevant disciplines like statistics, psychology, and computer science that contribute to its development.
Machine Learning SPPU Unit 1
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.
Machine learning
Machine learning is a subset of artificial intelligence that allows computers to learn without being explicitly programmed by improving their performance on tasks based on experience. It involves developing algorithms that can learn from and make predictions on data. There are many machine learning algorithms that differ in their representation, evaluation, and optimization methods, and algorithms can perform supervised learning (classification and regression), unsupervised learning (clustering and dimensionality reduction), semi-supervised learning, and reinforcement learning. Machine learning has applications in areas like web search, finance, e-commerce, robotics, and healthcare.
Intro/Overview on Machine Learning Presentation
This document provides an overview of a presentation on machine learning given at Gurukul Kangri University in 2017. It defines machine learning as a field that allows computers to learn without being explicitly programmed. It discusses different machine learning algorithms including supervised learning, unsupervised learning, and semi-supervised learning. Examples of applications of machine learning discussed include data mining, natural language processing, image recognition, and expert systems. The document also contrasts artificial intelligence, machine learning, and deep learning.
Eick/Alpaydin Introduction
Machine learning involves using algorithms to optimize performance using example data or past experience. It is useful when human expertise does not exist, cannot be explained, or needs to adapt over time. The document discusses different types of machine learning including supervised learning techniques like classification and regression as well as unsupervised learning techniques like clustering. It provides examples of applications in various domains and lists resources for datasets, journals, and conferences in the machine learning field.
How Python can be used for machine learning?
I would suggest you can use the python code for machine learning algorithms, in this presentation to easily implement and explore code in your projects.
Read more https://www.slideshare.net/nexsoftsys/why-do-we-use-python-and-ml-ai
Intro/Overview on Machine Learning Presentation -2
Intro/Overview on Machine Learning Presentation -2,
project on Machine Learning ,Intro/Overview on Machine Learning Presentation
Machine learning ppt
The presentation provides an overview of machine learning, including its history, definitions, applications and algorithms. It discusses how machine learning systems are trained and tested, and how performance is evaluated. The key points are that machine learning involves computers learning from experience to improve their abilities, it is used in applications that require prediction, classification and pattern detection, and common algorithms include supervised, unsupervised and reinforcement learning.
Machine learning
it covers the introduction of machine learning and variuos feilds that are related with machine learning.
Machine Learning
Machine learning involves programming computers to optimize performance using example data or past experience. It is used when human expertise does not exist, humans cannot explain their expertise, solutions change over time, or solutions need to be adapted to particular cases. Learning builds general models from data to approximate real-world examples. There are several types of machine learning including supervised learning (classification, regression), unsupervised learning (clustering), and reinforcement learning. Machine learning has applications in many domains including retail, finance, manufacturing, medicine, web mining, and more.
Machine learning ppt
In the past few years, India has witnessed exponential growth in the sector of Data Science. With the advent of digital transformation in businesses, the demand for data scientists is boosting every day with a ton of job opportunities machine learning course in mumbai’machine learning course in mumbais lying in their path. Boston Institute of Analytics provides data science courses in Mumbai. They train students under experienced industry professionals and make them industry ready. To know more about their courses check out their website https://www.biaclassroom.com/courses.
Machine learning
A PPT which gives a brief introduction on Machine Learning and on the products developed by using Machine Learning Algorithms in them. Gives the introduction by using content and also by using a few images in the slides as part of the explanation. It includes some examples of cool products like Google Cloud Platform, Cozmo (a tiny robot built by using Artificial Intelligence), IBM Watson and many more.
Machine learning Presentation
Machine learning is a branch of artificial intelligence. In which computers study algorithms. If I say in simple terms, machine learning is a computer algorithm study method that allows computer programs to learn from their experience. Now the question arises what is the algorithm.
https://www.viewofpeoples.xyz/2020/08/What-is-machine-learning.html
Types of Machine Learning
This presentation will give you the information about the types of Machine learning types and its algorithms.
Machine learning
Machine learning is a method of data analysis that uses algorithms to iteratively learn from data without being explicitly programmed. It allows computers to find hidden insights in data and become better at tasks via experience. Machine learning has many practical applications and is important due to growing data availability, cheaper and more powerful computation, and affordable storage. It is used in fields like finance, healthcare, marketing and transportation. The main approaches are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Each has real-world examples like loan prediction, market basket analysis, webpage classification, and marketing campaign optimization.
Machine learning
Machine learning works by processing data to discover patterns that can be used to analyze new data. Popular programming languages for machine learning include Python, R, and SQL. There are several types of machine learning including supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and deep learning. Common machine learning tasks involve classification, regression, clustering, dimensionality reduction, and model selection. Machine learning is widely used for applications such as spam filtering, recommendations, speech recognition, and machine translation.
Essential concepts for machine learning
This document provides an overview of machine learning, including definitions, types, steps, and applications. It defines machine learning as a field that gives computers the ability to learn without being explicitly programmed. The document outlines the main types of machine learning as supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. It also describes the typical steps in a machine learning process as gathering data, preparing data, choosing a model, training, evaluation, and prediction. Examples of machine learning applications discussed include prediction, image recognition, natural language processing, and personal assistants. Popular machine learning languages and packages are also listed.
Machine learning
Machine learning is a subset of artificial intelligence that allows computer systems to learn from data without being explicitly programmed. It involves the use of algorithms to recognize patterns in data in order to make predictions or decisions without being explicitly programmed to perform the specific tasks. There are two main types of machine learning: supervised learning which uses labeled data to predict outputs, and unsupervised learning which finds hidden patterns in unlabeled data. Machine learning has many applications and enables organizations to analyze complex data automatically to make data-driven decisions.
Overview of machine learning
This document provides an overview of machine learning. It defines machine learning as a branch of artificial intelligence concerned with designing algorithms that allow computers to evolve behaviors based on empirical data. The document outlines machine learning structures, including supervised and unsupervised learning. It discusses learning techniques such as perceptrons, logistic regression, and support vector machines. Finally, it provides examples of machine learning applications like face detection and economic/commercial usage.
Machine Learning Algorithms
The document discusses machine learning algorithms and provides descriptions of the top 10 algorithms. It begins by explaining the types of machine learning algorithms: supervised, unsupervised, and reinforcement learning. It then provides brief overviews of some of the most commonly used algorithms, including Naive Bayes, K-means clustering, support vector machines, Apriori, and others. For each algorithm, it gives a short description and links to additional resources.
Advantages and disadvantages of machine learning language
This document discusses the advantages and disadvantages of machine learning language. Some key advantages are that machine learning can be applied across many industries like banking, healthcare and retail. It allows for the processing of large and complex data and can reduce time cycles and utilize resources efficiently. However, some disadvantages are that acquiring and interpreting large datasets for machine learning algorithms can be challenging. Machine learning also requires a lot of training data and may be limited in unpredictable situations since it relies on historical data patterns. It can also be susceptible to errors that are difficult to diagnose and correct.
Machine learning
Machine learning is a type of artificial intelligence that allows systems to learn and improve automatically through experience. Arthur Samuel coined the term "machine learning" in 1959. Machine learning has advantages like identifying patterns in large datasets, improving continuously over time, and handling multidimensional data autonomously. However, it also has disadvantages such as requiring large, high-quality datasets and time to train accurately while being susceptible to errors from incomplete training data. Machine learning has applications in virtual assistants, surveillance, search results, and email/malware filtering.
10 Slide Intro to ML
First Slideshare Presentation
A very very small introduction to Machine Learning and providing a very small overview of the context. Curated from the Internet and other sources.
Machine Learning
Machine Learning. What is machine learning. Normal computer vs ML. Types of Machine Learning. Some ML Object detection methods. Faster CNN, RCNN, YOLO, SSD. Real Life ML Applications. Best Programming Languages for ML. Difference Between Machine Learning And Artificial Intelligence. Advantages of Machine Learning. Disadvantages of Machine Learning
Machine Learning an Research Overview
This document provides an overview of machine learning and artificial intelligence concepts. It discusses what machine learning is, including how machines can learn from examples to optimize performance without being explicitly programmed. Various machine learning algorithms and applications are covered, such as supervised learning techniques like classification and regression, as well as unsupervised learning and reinforcement learning. The goal of machine learning is to develop models that can make accurate predictions on new data based on patterns discovered from training data.
Machine Learning Contents.pptx
1. The document discusses machine learning types including supervised learning, unsupervised learning, and reinforcement learning. It provides examples of applications like spam filtering, recommendations, and fraud detection.
2. Key challenges in machine learning are discussed such as poor quality data, lack of training data, and imperfections when data grows.
3. The difference between data science and machine learning is explained - data science is a broader field that includes extracting insights from data using tools and models, while machine learning focuses specifically on making predictions using algorithms.
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Eick/Alpaydin Introduction
Machine learning involves using algorithms to optimize performance using example data or past experience. It is useful when human expertise does not exist, cannot be explained, or needs to adapt over time. The document discusses different types of machine learning including supervised learning techniques like classification and regression as well as unsupervised learning techniques like clustering. It provides examples of applications in various domains and lists resources for datasets, journals, and conferences in the machine learning field.
How Python can be used for machine learning?
I would suggest you can use the python code for machine learning algorithms, in this presentation to easily implement and explore code in your projects.
Read more https://www.slideshare.net/nexsoftsys/why-do-we-use-python-and-ml-ai
Intro/Overview on Machine Learning Presentation -2
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Machine learning ppt
The presentation provides an overview of machine learning, including its history, definitions, applications and algorithms. It discusses how machine learning systems are trained and tested, and how performance is evaluated. The key points are that machine learning involves computers learning from experience to improve their abilities, it is used in applications that require prediction, classification and pattern detection, and common algorithms include supervised, unsupervised and reinforcement learning.
Machine learning
it covers the introduction of machine learning and variuos feilds that are related with machine learning.
Machine Learning
Machine learning involves programming computers to optimize performance using example data or past experience. It is used when human expertise does not exist, humans cannot explain their expertise, solutions change over time, or solutions need to be adapted to particular cases. Learning builds general models from data to approximate real-world examples. There are several types of machine learning including supervised learning (classification, regression), unsupervised learning (clustering), and reinforcement learning. Machine learning has applications in many domains including retail, finance, manufacturing, medicine, web mining, and more.
Machine learning ppt
In the past few years, India has witnessed exponential growth in the sector of Data Science. With the advent of digital transformation in businesses, the demand for data scientists is boosting every day with a ton of job opportunities machine learning course in mumbai’machine learning course in mumbais lying in their path. Boston Institute of Analytics provides data science courses in Mumbai. They train students under experienced industry professionals and make them industry ready. To know more about their courses check out their website https://www.biaclassroom.com/courses.
Machine learning
A PPT which gives a brief introduction on Machine Learning and on the products developed by using Machine Learning Algorithms in them. Gives the introduction by using content and also by using a few images in the slides as part of the explanation. It includes some examples of cool products like Google Cloud Platform, Cozmo (a tiny robot built by using Artificial Intelligence), IBM Watson and many more.
Machine learning Presentation
Machine learning is a branch of artificial intelligence. In which computers study algorithms. If I say in simple terms, machine learning is a computer algorithm study method that allows computer programs to learn from their experience. Now the question arises what is the algorithm.
https://www.viewofpeoples.xyz/2020/08/What-is-machine-learning.html
Types of Machine Learning
This presentation will give you the information about the types of Machine learning types and its algorithms.
Machine learning
Machine learning is a method of data analysis that uses algorithms to iteratively learn from data without being explicitly programmed. It allows computers to find hidden insights in data and become better at tasks via experience. Machine learning has many practical applications and is important due to growing data availability, cheaper and more powerful computation, and affordable storage. It is used in fields like finance, healthcare, marketing and transportation. The main approaches are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Each has real-world examples like loan prediction, market basket analysis, webpage classification, and marketing campaign optimization.
Machine learning
Machine learning works by processing data to discover patterns that can be used to analyze new data. Popular programming languages for machine learning include Python, R, and SQL. There are several types of machine learning including supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and deep learning. Common machine learning tasks involve classification, regression, clustering, dimensionality reduction, and model selection. Machine learning is widely used for applications such as spam filtering, recommendations, speech recognition, and machine translation.
Essential concepts for machine learning
This document provides an overview of machine learning, including definitions, types, steps, and applications. It defines machine learning as a field that gives computers the ability to learn without being explicitly programmed. The document outlines the main types of machine learning as supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. It also describes the typical steps in a machine learning process as gathering data, preparing data, choosing a model, training, evaluation, and prediction. Examples of machine learning applications discussed include prediction, image recognition, natural language processing, and personal assistants. Popular machine learning languages and packages are also listed.
Machine learning
Machine learning is a subset of artificial intelligence that allows computer systems to learn from data without being explicitly programmed. It involves the use of algorithms to recognize patterns in data in order to make predictions or decisions without being explicitly programmed to perform the specific tasks. There are two main types of machine learning: supervised learning which uses labeled data to predict outputs, and unsupervised learning which finds hidden patterns in unlabeled data. Machine learning has many applications and enables organizations to analyze complex data automatically to make data-driven decisions.
Overview of machine learning
This document provides an overview of machine learning. It defines machine learning as a branch of artificial intelligence concerned with designing algorithms that allow computers to evolve behaviors based on empirical data. The document outlines machine learning structures, including supervised and unsupervised learning. It discusses learning techniques such as perceptrons, logistic regression, and support vector machines. Finally, it provides examples of machine learning applications like face detection and economic/commercial usage.
Machine Learning Algorithms
The document discusses machine learning algorithms and provides descriptions of the top 10 algorithms. It begins by explaining the types of machine learning algorithms: supervised, unsupervised, and reinforcement learning. It then provides brief overviews of some of the most commonly used algorithms, including Naive Bayes, K-means clustering, support vector machines, Apriori, and others. For each algorithm, it gives a short description and links to additional resources.
Advantages and disadvantages of machine learning language
This document discusses the advantages and disadvantages of machine learning language. Some key advantages are that machine learning can be applied across many industries like banking, healthcare and retail. It allows for the processing of large and complex data and can reduce time cycles and utilize resources efficiently. However, some disadvantages are that acquiring and interpreting large datasets for machine learning algorithms can be challenging. Machine learning also requires a lot of training data and may be limited in unpredictable situations since it relies on historical data patterns. It can also be susceptible to errors that are difficult to diagnose and correct.
Machine learning
Machine learning is a type of artificial intelligence that allows systems to learn and improve automatically through experience. Arthur Samuel coined the term "machine learning" in 1959. Machine learning has advantages like identifying patterns in large datasets, improving continuously over time, and handling multidimensional data autonomously. However, it also has disadvantages such as requiring large, high-quality datasets and time to train accurately while being susceptible to errors from incomplete training data. Machine learning has applications in virtual assistants, surveillance, search results, and email/malware filtering.
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This document provides an overview of machine learning and artificial intelligence concepts. It discusses what machine learning is, including how machines can learn from examples to optimize performance without being explicitly programmed. Various machine learning algorithms and applications are covered, such as supervised learning techniques like classification and regression, as well as unsupervised learning and reinforcement learning. The goal of machine learning is to develop models that can make accurate predictions on new data based on patterns discovered from training data.
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1. The document discusses machine learning types including supervised learning, unsupervised learning, and reinforcement learning. It provides examples of applications like spam filtering, recommendations, and fraud detection.
2. Key challenges in machine learning are discussed such as poor quality data, lack of training data, and imperfections when data grows.
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Machine learning involves computers improving their ability to complete tasks through experience. A machine learning problem is well-defined if it identifies: 1) the class of tasks, 2) a performance measure to improve on, and 3) the source of training experience. For example, a program that learns to play checkers would improve its ability to win games (performance measure) by playing practice games against itself (training experience) for checkers games (class of tasks). How machines learn involves inputting past data, abstracting that data using algorithms, and generalizing the abstraction to make decisions.
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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.
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This document provides an overview of machine learning. It defines machine learning as a field that allows computers to learn without being explicitly programmed and discusses how machine learning involves automating automation by allowing computers to program themselves. The document outlines different machine learning algorithms such as supervised, unsupervised, and reinforcement learning. It also discusses applications of machine learning such as object recognition, search engines, and robot locomotion. Finally, the document provides examples of machine learning software suites and concludes that machine learning will play a big role in allowing machines to perform tasks like humans.
Machine learning
Machine learning is a science that allows computers to learn from data and improve their abilities over time without being explicitly programmed. It works by feeding computers data and information in the form of observations and interactions to learn patterns and make data-driven decisions. Machine learning algorithms can figure out how to perform tasks by generalizing from examples provided in the training data.
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Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
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2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
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6:个人职称评审加20分
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8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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Visit Us For : https://www.dronainfotech.com/mobile-application-development/
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Web:- https://undressbaby.com/
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Machine learning
- 1. Presentation Machine Learning Wahid Ur Rehman Roll number CS-16 BSC 5th semester Govt Science Superior College
- 2. What is machine learning? Training and testing Performance Machine learning structure What are we seeking? Learning techniques Applications Outline & Content
- 3. A branch of artificial intelligence, concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data. As intelligence requires knowledge, it is necessary for the computers to acquire knowledge. What is machine learning?
- 4. Machine Learning is… “Machine learning is programming computers to optimize a performance criterion using example data or past experience”. -- Ethem Alpaydin “The goal of machine learning is to develop methods that can automatically detect patterns in data, and then to use the uncovered patterns to predict future data or other outcomes of interest”. -- Kevin P. Murphy “The field of pattern recognition is concerned with the automatic discovery of regularities in data through the use of computer algorithms and with the use of these regularities to take actions”. -- Christopher M. Bishop
- 5. Traditional Programming Machine Learning Computer Data Program Output Computer Data Output Program
- 7. Magic? No, more like gardening Seeds = Algorithms Nutrients = Data Gardener = You Plants = Programs
- 8. A classic example of a task that requires machine learning: It is very hard to say what makes a 2
- 9. Growth of Machine Learning Machine learning is preferred approach to Speech recognition, Natural language processing Computer vision Medical outcomes analysis Robot control Computational biology This trend is accelerating Improved machine learning algorithms Improved data capture, networking, faster computers Software too complex to write by hand New sensors / IO devices Demand for self-customization to user, environment
- 10. Types of Machine Learning Supervised Learning Un-supervised Learning Semi-Supervised Learning Reinforcement Learning
- 11. Types of Learning Supervised (inductive) learning Training data includes desired outputs Unsupervised learning Training data does not include desired outputs Semi-supervised learning Training data includes a few desired outputs Reinforcement learning Rewards from sequence of actions
- 12. ML in Algorithm Tens of thousands of machine learning algorithms Hundreds new every year Every machine learning algorithm has three components: Representation Evaluation Optimization
- 13. Representation Decision trees Sets of rules / Logic programs Instances Graphical models (Bayes/Markov nets) Neural networks Support vector machines Model ensembles Etc.
- 14. Evaluation Accuracy Precision and recall Squared error Likelihood Posterior probability Cost / Utility Margin Entropy K-L divergence Etc.
- 15. Optimization/Search Uses best search algorithm Such like Breadth first,Genetic ect
- 16. 5-Step Systematic Process Define the Problem Prepare Data Spot Check Algorithms Improve Results Present Results
- 18. Let's take a look at some of the top open source machine learning frameworks available Apache Singa. ... Shogun. ... Apache Mahout. ... Apache Spark MLlib. ... TensorFlow. ... Oryx 2. ... Accord.NET. Amazon Machine Learning (AML)
- 19. Learning techniques available Rote learning_______if no perior knowledge exist then learn from input Deductive learning________conculsion=from observation Deductive learning works on existing facts and knowledge and deduces new fro m old knowledge. Inductive learning _________reverse of Deductive