Machine learning is the study of algorithms that improve their performance on a task based on experience. The document discusses machine learning applications such as autonomous vehicles, speech recognition using deep learning, and supervised, unsupervised, and reinforcement learning. It also covers important concepts in machine learning like defining the learning task, representing functions, and designing learning systems.
ppt on introduction to Machine learning toolsRaviKiranVarma4
This document provides an introduction to the CIS 419/519 Introduction to Machine Learning course taught by Eric Eaton. It defines machine learning as a field that studies algorithms to improve performance on tasks based on experience. The document outlines key topics that will be covered in the course, including supervised learning techniques like decision trees, regression, and neural networks, as well as unsupervised learning, reinforcement learning, and evaluation methods. Real-world applications of machine learning are discussed.
This document provides an overview of machine learning. It begins by defining machine learning as improving performance on some task based on experience. Traditional programming is distinguished from machine learning by how the computer learns. Sample applications are discussed such as web search, computational biology, and robotics. Classic examples of machine learning tasks are discussed like playing checkers and recognizing handwritten words. The document then covers state of the art applications like autonomous vehicles, deep learning, and speech recognition. Different types of learning are introduced like supervised, unsupervised, and reinforcement learning. Finally, the document discusses designing a learning system by choosing the training experience, representation, learning algorithm, and evaluation method.
This document provides an introduction to machine learning. It defines machine learning as a field of study that allows computers to learn without being explicitly programmed. The document discusses different types of machine learning tasks including supervised learning, unsupervised learning, reinforcement learning, and inverse reinforcement learning. It also covers common machine learning applications, function representations, optimization algorithms, and evaluation metrics.
This document provides an introduction to machine learning. It defines machine learning as a field of study that allows computers to learn without being explicitly programmed. Machine learning involves improving performance on some task based on experience. Supervised learning, unsupervised learning, reinforcement learning, and their applications are discussed. Key aspects of designing a machine learning system like choosing the learning task, representation, and algorithm are also covered. Examples of machine learning applications in areas like autonomous vehicles, speech recognition, and computer vision are provided.
Machine learning involves algorithms that improve their performance on a task based on experience. It is used when human expertise does not exist, cannot be explained, must be customized for large amounts of data. Examples of tasks well-suited for machine learning include pattern recognition, generation, anomaly detection, and prediction. Machine learning can be supervised (classification, regression), unsupervised (clustering), reinforcement, or inverse reinforcement learning. Designing a learning system involves choosing the training experience, target function, representation, and learning algorithm. Evaluation metrics depend on the problem and domain.
1. Dr. R. Gunavathi of the PG and Research Department of Computer Applications at [institution name redacted] organized a seminar on IoT applications and machine learning.
2. The seminar featured a presentation by Assistant Professor Sushama of JECRC University on machine learning and its applications.
3. Machine learning involves using algorithms to improve performance on tasks based on experience. It is commonly used when human expertise is limited, models must be customized, or huge amounts of data are involved.
1) Machine learning is the study of algorithms that improve at tasks through experience. It is used when human expertise is limited, models must be customized, or huge amounts of data are available.
2) There are several types of learning, including supervised learning (classification and regression), unsupervised learning (clustering, dimensionality reduction), and reinforcement learning (learning from rewards/penalties).
3) A machine learning problem involves defining the learning task, experience, and performance metric, and choosing a representation and learning algorithm to infer the target function from the data.
This document provides an introduction to machine learning, including definitions, applications, and types of learning. It defines machine learning as the study of algorithms that improve performance on tasks with experience. The main types of learning covered are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Supervised learning uses labeled training data, unsupervised learning uses unlabeled data, and reinforcement learning involves sequences of actions with rewards. Machine learning has many applications and the field is growing rapidly.
ppt on introduction to Machine learning toolsRaviKiranVarma4
This document provides an introduction to the CIS 419/519 Introduction to Machine Learning course taught by Eric Eaton. It defines machine learning as a field that studies algorithms to improve performance on tasks based on experience. The document outlines key topics that will be covered in the course, including supervised learning techniques like decision trees, regression, and neural networks, as well as unsupervised learning, reinforcement learning, and evaluation methods. Real-world applications of machine learning are discussed.
This document provides an overview of machine learning. It begins by defining machine learning as improving performance on some task based on experience. Traditional programming is distinguished from machine learning by how the computer learns. Sample applications are discussed such as web search, computational biology, and robotics. Classic examples of machine learning tasks are discussed like playing checkers and recognizing handwritten words. The document then covers state of the art applications like autonomous vehicles, deep learning, and speech recognition. Different types of learning are introduced like supervised, unsupervised, and reinforcement learning. Finally, the document discusses designing a learning system by choosing the training experience, representation, learning algorithm, and evaluation method.
This document provides an introduction to machine learning. It defines machine learning as a field of study that allows computers to learn without being explicitly programmed. The document discusses different types of machine learning tasks including supervised learning, unsupervised learning, reinforcement learning, and inverse reinforcement learning. It also covers common machine learning applications, function representations, optimization algorithms, and evaluation metrics.
This document provides an introduction to machine learning. It defines machine learning as a field of study that allows computers to learn without being explicitly programmed. Machine learning involves improving performance on some task based on experience. Supervised learning, unsupervised learning, reinforcement learning, and their applications are discussed. Key aspects of designing a machine learning system like choosing the learning task, representation, and algorithm are also covered. Examples of machine learning applications in areas like autonomous vehicles, speech recognition, and computer vision are provided.
Machine learning involves algorithms that improve their performance on a task based on experience. It is used when human expertise does not exist, cannot be explained, must be customized for large amounts of data. Examples of tasks well-suited for machine learning include pattern recognition, generation, anomaly detection, and prediction. Machine learning can be supervised (classification, regression), unsupervised (clustering), reinforcement, or inverse reinforcement learning. Designing a learning system involves choosing the training experience, target function, representation, and learning algorithm. Evaluation metrics depend on the problem and domain.
1. Dr. R. Gunavathi of the PG and Research Department of Computer Applications at [institution name redacted] organized a seminar on IoT applications and machine learning.
2. The seminar featured a presentation by Assistant Professor Sushama of JECRC University on machine learning and its applications.
3. Machine learning involves using algorithms to improve performance on tasks based on experience. It is commonly used when human expertise is limited, models must be customized, or huge amounts of data are involved.
1) Machine learning is the study of algorithms that improve at tasks through experience. It is used when human expertise is limited, models must be customized, or huge amounts of data are available.
2) There are several types of learning, including supervised learning (classification and regression), unsupervised learning (clustering, dimensionality reduction), and reinforcement learning (learning from rewards/penalties).
3) A machine learning problem involves defining the learning task, experience, and performance metric, and choosing a representation and learning algorithm to infer the target function from the data.
This document provides an introduction to machine learning, including definitions, applications, and types of learning. It defines machine learning as the study of algorithms that improve performance on tasks with experience. The main types of learning covered are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Supervised learning uses labeled training data, unsupervised learning uses unlabeled data, and reinforcement learning involves sequences of actions with rewards. Machine learning has many applications and the field is growing rapidly.
L 8 introduction to machine learning final kirti.pptxKirti Verma
Machine learning is the study of algorithms that improve performance on tasks based on experience. There are different types of machine learning including supervised learning (classification and regression), unsupervised learning (clustering), and reinforcement learning. Machine learning has many applications such as autonomous vehicles, speech recognition, computer vision, and bioinformatics. Deep learning is a new area of machine learning using neural networks that has achieved state-of-the-art results in areas like speech recognition and computer vision.
This document provides an overview of machine learning concepts. It defines machine learning as creating computer programs that improve with experience. Supervised learning uses labeled training data to build models that can classify or predict new examples, while unsupervised learning finds patterns in unlabeled data. Examples of machine learning applications include spam filtering, recommendation systems, and medical diagnosis. The document also discusses important machine learning techniques like k-nearest neighbors, decision trees, regularization, and cross-validation.
This document provides an overview of machine learning concepts including:
1. Machine learning aims to create computer programs that improve with experience by learning from data. It involves tasks like classification, regression, and clustering.
2. Data comes in different types like text, numbers, images and is generated in massive quantities daily from sources like Google, Facebook, and sensors.
3. Machine learning algorithms are either supervised, using labeled training data, or unsupervised, using unlabeled data. Common supervised techniques are decision trees, neural networks, and support vector machines while clustering is a major unsupervised technique.
Introduction to machine learning-2023-IT-AI and DS.pdfSisayNegash4
This document provides an overview of machine learning including definitions, applications, related fields, and challenges. It defines machine learning as computer programs that automatically learn from experience to improve their performance on tasks without being explicitly programmed. Key points include:
- Machine learning aims to extract patterns from complex data and build models to solve problems.
- It has applications in areas like image recognition, natural language processing, prediction, and more.
- Probability and statistics are fundamental to machine learning for dealing with uncertainty in data.
- Machine learning problems can be classified as supervised, unsupervised, semi-supervised, or reinforcement learning.
- Challenges include scaling algorithms to large datasets, handling high-dimensional data, and addressing noise and
The document provides an overview of machine learning concepts including:
- Defining machine learning as computer algorithms that improve with experience and data
- Describing examples like speech recognition, medical diagnosis, and game playing
- Outlining the components of designing a machine learning system like choosing the target function, representation, and learning algorithm
- Explaining concepts like version spaces, decision trees, and neural networks that are used in machine learning applications
A comprehensive introduction to machine learning and deep learning along with application in finance (provided by an example of predicting bank failure). Then, the difference of ML in tech and ML in finance is outlined. Last section is excluded from the file.
1. Machine learning involves using algorithms to learn from data without being explicitly programmed. It is an interdisciplinary field that draws from statistics, computer science, and many other areas.
2. There are massive amounts of data being generated every day from sources like Google, Facebook, YouTube, and more. This data provides opportunities for machine learning applications.
3. Machine learning tasks can be supervised, involving labeled example data, or unsupervised, involving unlabeled data. Supervised learning predicts labels for new data based on patterns in labeled training data, while unsupervised learning finds hidden patterns in unlabeled data.
Bridging the Gap: Machine Learning for Ubiquitous Computing -- ML and Ubicomp...Thomas Ploetz
Tutorial @Ubicomp 2015: Bridging the Gap -- Machine Learning for Ubiquitous Computing (machine learning and ubicomp primer session).
A tutorial on promises and pitfalls of Machine Learning for Ubicomp (and Human Computer Interaction). From Practitioners for Practitioners.
Presenter: Thomas Ploetz <tom.ploetz@gmail.com>
video recording of talks as they were held at Ubicomp:
https://youtu.be/LgnnlqOIXJc?list=PLh96aGaacSgXw0MyktFqmgijLHN-aQvdq
Module1 of Introduction to Machine LearningMayuraD1
This document provides an overview of the "Introduction to Machine Learning" course, including:
- The course is worth 3 credits and takes place in the 2022-23 academic year.
- Module 1 covers what machine learning is, its history and applications, different categories of machine learning like supervised and unsupervised learning, and key terminology.
- Machine learning enables machines to learn from data, improve performance, and make predictions without being explicitly programmed. It is a subset of artificial intelligence focused on algorithm development.
Fundementals of Machine Learning and Deep Learning ParrotAI
Introduction to machine learning and deep learning to beginners.Learn the applications of machine learning and deep learning and how ti can solve different problems
This document provides information about a Machine Learning course, including its objectives, outcomes, references, prerequisites, and content. The course aims to enable students to define machine learning, differentiate between different learning techniques, understand concepts like decision trees and neural networks, and perform statistical analysis of machine learning models. The content is divided into 5 modules covering topics such as introduction, concept learning, decision trees, neural networks, Bayesian learning, and more. References include the instructor's webpage and textbooks on machine learning and statistical learning. Prerequisites include basic programming skills and knowledge of algorithms, probability, and statistics.
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.
This document provides an overview of machine learning concepts including:
- Defining machine learning as the study of how to build systems that improve with experience.
- Designing a learning system for the task of playing checkers by choosing the training experience, representation, and learning algorithm.
- Common machine learning applications like speech recognition, computer vision, and robot control.
Lecture 01: Machine Learning for Language Technology - IntroductionMarina Santini
This document provides an introduction to a machine learning course being taught at Uppsala University. It outlines the schedule, reading list, assignments, and examination. The course covers topics like decision trees, linear models, ensemble methods, text mining, and unsupervised learning. It discusses the differences between supervised and unsupervised learning, as well as classification, regression, and other machine learning techniques. The goal is to introduce students to commonly used methods in natural language processing.
Machine learning and deep learning techniques can be used to analyze diverse types of data such as images, text, signals and more. Deep learning uses neural networks to learn directly from raw data, enabling applications like object recognition, speech recognition, and analyzing time series signals. Deep learning has become popular due to labeled public datasets, increased GPU acceleration, and pre-trained models that provide a starting point for new problems.
Deep Learning And Business Models (VNITC 2015-09-13)Ha Phuong
Deep Learning and Business Models
Tran Quoc Hoan discusses deep learning and its applications, as well as potential business models. Deep learning has led to significant improvements in areas like image and speech recognition compared to traditional machine learning. Some business models highlighted include developing deep learning frameworks, building hardware optimized for deep learning, using deep learning for IoT applications, and providing deep learning APIs and services. Deep learning shows promise across many sectors but also faces challenges in fully realizing its potential.
Machine learning involves using data to answer questions and make predictions. There are three main types of machine learning problems: supervised learning which involves predicting outputs given labeled examples; unsupervised learning which finds hidden patterns in unlabeled data; and reinforcement learning where an agent learns through trial-and-error interactions with an environment. To solve a machine learning problem typically involves five steps: gathering and preprocessing data, engineering features, selecting and training an algorithm, and using the trained model to make predictions.
- The document discusses a lecture on machine learning given by Ravi Gupta and G. Bharadwaja Kumar.
- Machine learning allows computers to automatically improve at tasks through experience. It is used for problems where the output is unknown and computation is expensive.
- Machine learning involves training a decision function or hypothesis on examples to perform tasks like classification, regression, and clustering. The training experience and representation impact whether learning succeeds.
- Choosing how to represent the target function, select training examples, and update weights to improve performance are issues in machine learning systems.
Machine learning is a form of artificial intelligence that allows systems to automatically learn and improve from experience without being explicitly programmed. It focuses on developing computer programs that can access data and use it to learn on their own. A key factor in the recent boom in machine learning has been the availability of large amounts of data and high-powered computers. Machine learning algorithms are used across many domains including cancer detection, text mining, business intelligence, and self-driving cars. There are two main types of machine learning: supervised learning, which uses labeled data to train classifiers or regression models, and unsupervised learning, which finds hidden patterns in unlabeled data using clustering. Deep learning is a modern technique that uses neural networks with many layers to perform complex classification
L 8 introduction to machine learning final kirti.pptxKirti Verma
Machine learning is the study of algorithms that improve performance on tasks based on experience. There are different types of machine learning including supervised learning (classification and regression), unsupervised learning (clustering), and reinforcement learning. Machine learning has many applications such as autonomous vehicles, speech recognition, computer vision, and bioinformatics. Deep learning is a new area of machine learning using neural networks that has achieved state-of-the-art results in areas like speech recognition and computer vision.
This document provides an overview of machine learning concepts. It defines machine learning as creating computer programs that improve with experience. Supervised learning uses labeled training data to build models that can classify or predict new examples, while unsupervised learning finds patterns in unlabeled data. Examples of machine learning applications include spam filtering, recommendation systems, and medical diagnosis. The document also discusses important machine learning techniques like k-nearest neighbors, decision trees, regularization, and cross-validation.
This document provides an overview of machine learning concepts including:
1. Machine learning aims to create computer programs that improve with experience by learning from data. It involves tasks like classification, regression, and clustering.
2. Data comes in different types like text, numbers, images and is generated in massive quantities daily from sources like Google, Facebook, and sensors.
3. Machine learning algorithms are either supervised, using labeled training data, or unsupervised, using unlabeled data. Common supervised techniques are decision trees, neural networks, and support vector machines while clustering is a major unsupervised technique.
Introduction to machine learning-2023-IT-AI and DS.pdfSisayNegash4
This document provides an overview of machine learning including definitions, applications, related fields, and challenges. It defines machine learning as computer programs that automatically learn from experience to improve their performance on tasks without being explicitly programmed. Key points include:
- Machine learning aims to extract patterns from complex data and build models to solve problems.
- It has applications in areas like image recognition, natural language processing, prediction, and more.
- Probability and statistics are fundamental to machine learning for dealing with uncertainty in data.
- Machine learning problems can be classified as supervised, unsupervised, semi-supervised, or reinforcement learning.
- Challenges include scaling algorithms to large datasets, handling high-dimensional data, and addressing noise and
The document provides an overview of machine learning concepts including:
- Defining machine learning as computer algorithms that improve with experience and data
- Describing examples like speech recognition, medical diagnosis, and game playing
- Outlining the components of designing a machine learning system like choosing the target function, representation, and learning algorithm
- Explaining concepts like version spaces, decision trees, and neural networks that are used in machine learning applications
A comprehensive introduction to machine learning and deep learning along with application in finance (provided by an example of predicting bank failure). Then, the difference of ML in tech and ML in finance is outlined. Last section is excluded from the file.
1. Machine learning involves using algorithms to learn from data without being explicitly programmed. It is an interdisciplinary field that draws from statistics, computer science, and many other areas.
2. There are massive amounts of data being generated every day from sources like Google, Facebook, YouTube, and more. This data provides opportunities for machine learning applications.
3. Machine learning tasks can be supervised, involving labeled example data, or unsupervised, involving unlabeled data. Supervised learning predicts labels for new data based on patterns in labeled training data, while unsupervised learning finds hidden patterns in unlabeled data.
Bridging the Gap: Machine Learning for Ubiquitous Computing -- ML and Ubicomp...Thomas Ploetz
Tutorial @Ubicomp 2015: Bridging the Gap -- Machine Learning for Ubiquitous Computing (machine learning and ubicomp primer session).
A tutorial on promises and pitfalls of Machine Learning for Ubicomp (and Human Computer Interaction). From Practitioners for Practitioners.
Presenter: Thomas Ploetz <tom.ploetz@gmail.com>
video recording of talks as they were held at Ubicomp:
https://youtu.be/LgnnlqOIXJc?list=PLh96aGaacSgXw0MyktFqmgijLHN-aQvdq
Module1 of Introduction to Machine LearningMayuraD1
This document provides an overview of the "Introduction to Machine Learning" course, including:
- The course is worth 3 credits and takes place in the 2022-23 academic year.
- Module 1 covers what machine learning is, its history and applications, different categories of machine learning like supervised and unsupervised learning, and key terminology.
- Machine learning enables machines to learn from data, improve performance, and make predictions without being explicitly programmed. It is a subset of artificial intelligence focused on algorithm development.
Fundementals of Machine Learning and Deep Learning ParrotAI
Introduction to machine learning and deep learning to beginners.Learn the applications of machine learning and deep learning and how ti can solve different problems
This document provides information about a Machine Learning course, including its objectives, outcomes, references, prerequisites, and content. The course aims to enable students to define machine learning, differentiate between different learning techniques, understand concepts like decision trees and neural networks, and perform statistical analysis of machine learning models. The content is divided into 5 modules covering topics such as introduction, concept learning, decision trees, neural networks, Bayesian learning, and more. References include the instructor's webpage and textbooks on machine learning and statistical learning. Prerequisites include basic programming skills and knowledge of algorithms, probability, and statistics.
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.
This document provides an overview of machine learning concepts including:
- Defining machine learning as the study of how to build systems that improve with experience.
- Designing a learning system for the task of playing checkers by choosing the training experience, representation, and learning algorithm.
- Common machine learning applications like speech recognition, computer vision, and robot control.
Lecture 01: Machine Learning for Language Technology - IntroductionMarina Santini
This document provides an introduction to a machine learning course being taught at Uppsala University. It outlines the schedule, reading list, assignments, and examination. The course covers topics like decision trees, linear models, ensemble methods, text mining, and unsupervised learning. It discusses the differences between supervised and unsupervised learning, as well as classification, regression, and other machine learning techniques. The goal is to introduce students to commonly used methods in natural language processing.
Machine learning and deep learning techniques can be used to analyze diverse types of data such as images, text, signals and more. Deep learning uses neural networks to learn directly from raw data, enabling applications like object recognition, speech recognition, and analyzing time series signals. Deep learning has become popular due to labeled public datasets, increased GPU acceleration, and pre-trained models that provide a starting point for new problems.
Deep Learning And Business Models (VNITC 2015-09-13)Ha Phuong
Deep Learning and Business Models
Tran Quoc Hoan discusses deep learning and its applications, as well as potential business models. Deep learning has led to significant improvements in areas like image and speech recognition compared to traditional machine learning. Some business models highlighted include developing deep learning frameworks, building hardware optimized for deep learning, using deep learning for IoT applications, and providing deep learning APIs and services. Deep learning shows promise across many sectors but also faces challenges in fully realizing its potential.
Machine learning involves using data to answer questions and make predictions. There are three main types of machine learning problems: supervised learning which involves predicting outputs given labeled examples; unsupervised learning which finds hidden patterns in unlabeled data; and reinforcement learning where an agent learns through trial-and-error interactions with an environment. To solve a machine learning problem typically involves five steps: gathering and preprocessing data, engineering features, selecting and training an algorithm, and using the trained model to make predictions.
- The document discusses a lecture on machine learning given by Ravi Gupta and G. Bharadwaja Kumar.
- Machine learning allows computers to automatically improve at tasks through experience. It is used for problems where the output is unknown and computation is expensive.
- Machine learning involves training a decision function or hypothesis on examples to perform tasks like classification, regression, and clustering. The training experience and representation impact whether learning succeeds.
- Choosing how to represent the target function, select training examples, and update weights to improve performance are issues in machine learning systems.
Machine learning is a form of artificial intelligence that allows systems to automatically learn and improve from experience without being explicitly programmed. It focuses on developing computer programs that can access data and use it to learn on their own. A key factor in the recent boom in machine learning has been the availability of large amounts of data and high-powered computers. Machine learning algorithms are used across many domains including cancer detection, text mining, business intelligence, and self-driving cars. There are two main types of machine learning: supervised learning, which uses labeled data to train classifiers or regression models, and unsupervised learning, which finds hidden patterns in unlabeled data using clustering. Deep learning is a modern technique that uses neural networks with many layers to perform complex classification
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
2. What is Machine Learning?
“Learning is any process by which a system improves
performance from experience.”
- Herbert Simon
Definition by Tom Mitchell (1998):
Machine Learning is the study of algorithms that
• improve their performance P
• at some task T
• with experience E.
A well-defined learning task is given by <P, T, E>.
3
4. When Do We Use Machine Learning?
ML is used when:
• Human expertise does not exist (navigating on Mars)
• Humans can’t explain their expertise (speech recognition)
• Models must be customized (personalized medicine)
• Models are based on huge amounts of data (genomics)
Learning isn’t always useful:
• There is no need to “learn” to calculate payroll
Based on slide by E. Alpaydin
5
5. A classic example of a task that requires machine learning:
It is very hard to say what makes a 2
Slide credit: Geoffrey Hinton
6
6. Some more examples of tasks that are best
solved by using a learning algorithm
• Recognizing patterns:
– Facial identities or facial expressions
– Handwritten or spoken words
– Medical images
• Generating patterns:
– Generating images or motion sequences
• Recognizing anomalies:
– Unusual credit card transactions
– Unusual patterns of sensor readings in a nuclear power plant
• Prediction:
– Future stock prices or currency exchange rates
Slide credit: Geoffrey Hinton
7
7. Sample Applications
• Web search
• Computational biology
• Finance
• E-commerce
• Space exploration
• Robotics
• Information extraction
• Social networks
• Debugging software
• [Your favorite area]
Slide credit: Pedro Domingos
8
9. 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
Slide credit: Ray Mooney
10
10. State of the Art Applications of
Machine Learning
11
11. Autonomous Cars
Penn’s Autonomous Car à
(Ben Franklin Racing Team)
• Nevada made it legal for
autonomous cars to drive on
roads in June 2011
• As of 2013, four states (Nevada,
Florida, California, and
Michigan) have legalized
autonomous cars
12
13. Autonomous Car Technology
Laser Terrain Mapping
Sebastian
Stanley
Adaptive Vision
Learning from Human Drivers
Path
Planning
Images and movies taken from Sebastian Thrun’s multimedia website.
14
16. Examples of learned object parts from object categories
Learning of Object Parts
Faces Cars Elephants Chairs
Slide credit: Andrew Ng
17
17. Training on Multiple Objects
Trained on 4 classes (cars, faces,
motorbikes, airplanes).
Second layer: Shared-features
and object-specific features.
Third layer: More specific
features.
Slide credit: Andrew Ng
18
18. Scene Labeling via Deep Learning
[Farabet et al. ICML 2012, PAMI 2013] 19
19. Input images
Samples from
feedforward
Inference
(control)
Samples from
Full posterior
inference
Inference from Deep Learned Models
Generating posterior samples from faces by “filling in” experiments
(cf. Lee and Mumford, 2003). Combine bottom-up and top-down inference.
Slide credit: Andrew Ng
20
20. Machine Learning in
Automatic Speech Recognition
A Typical Speech Recognition System
ML used to predict of phone states from the sound spectrogram
Deep learning has state-of-the-art results
# Hidden Layers 1 2 4 8 10 12
Word Error Rate % 16.0 12.8 11.4 10.9 11.0 11.1
Baseline GMM performance = 15.4%
[Zeiler et al. “On rectified linear units for speech
recognition” ICASSP 2013]
21
21. Impact of Deep Learning in Speech Technology
Slide credit: Li Deng, MS Research
22
23. Types of Learning
• Supervised (inductive) learning
– Given: training data + desired outputs (labels)
• Unsupervised learning
– Given: training data (without desired outputs)
• Semi-supervised learning
– Given: training data + a few desired outputs
• Reinforcement learning
– Rewards from sequence of actions
Based on slide by Pedro Domingos
24
24. Supervised Learning: Regression
• Given (x1, y1), (x2, y2), ..., (xn, yn)
• Learn a function f(x) to predict y given x
– y is real-valued == regression
0
1
2
3
4
5
6
7
8
9
1970 1980 1990 2000 2010 2020
September
Arctic
Sea
Ice
Extent
(1,000,000
sq
km)
Year
Data from G. Witt. Journal of Statistics Education, Volume 21, Number 1 (2013)
26
25. Supervised Learning: Classification
• Given (x1, y1), (x2, y2), ..., (xn, yn)
• Learn a function f(x) to predict y given x
– y is categorical == classification
1(Malignant)
0(Benign)
Tumor Size
Breast Cancer (Malignant / Benign)
Based on example by Andrew Ng
27
26. Supervised Learning: Classification
• Given (x1, y1), (x2, y2), ..., (xn, yn)
• Learn a function f(x) to predict y given x
– y is categorical == classification
1(Malignant)
0(Benign)
Tumor Size
Breast Cancer (Malignant / Benign)
Tumor Size
Based on example by Andrew Ng
28
27. Supervised Learning: Classification
• Given (x1, y1), (x2, y2), ..., (xn, yn)
• Learn a function f(x) to predict y given x
– y is categorical == classification
1(Malignant)
0(Benign)
Tumor Size
Breast Cancer (Malignant / Benign)
Tumor Size
Based on example by Andrew Ng
Predict Malignant
Predict Benign
29
28. Supervised Learning
Tumor Size
Age
- Clump Thickness
- Uniformity of Cell Size
- Uniformity of Cell Shape
…
• x can be multi-dimensional
– Each dimension corresponds to an attribute
Based on example by Andrew Ng
30
31. Organize computing clusters Social network analysis
Image credit: NASA/JPL-Caltech/E. Churchwell (Univ. of Wisconsin, Madison)
Astronomical data analysis
Market segmentation
Slide credit: Andrew Ng
Unsupervised Learning
33
32. Unsupervised Learning
• Independent component analysis – separate a
combined signal into its original sources
Image credit: statsoft.com Audio from http://www.ism.ac.jp/~shiro/research/blindsep.html
34
33. Unsupervised Learning
• Independent component analysis – separate a
combined signal into its original sources
Image credit: statsoft.com Audio from http://www.ism.ac.jp/~shiro/research/blindsep.html
35
34. Reinforcement Learning
• Given a sequence of states and actions with
(delayed) rewards, output a policy
– Policy is a mapping from states à actions that
tells you what to do in a given state
• Examples:
– Credit assignment problem
– Game playing
– Robot in a maze
– Balance a pole on your hand
36
35. The Agent-Environment Interface
Agent and environment interact at discrete time steps : t = 0, 1, 2, K
Agent observes state at step t: st ∈S
produces action at step t : at ∈ A(st )
gets resulting reward : rt +1 ∈ℜ
and resulting next state : st +1
t
. . . st a
rt +1 st +1
t +1
a
rt +2 st +2
t +2
a
rt +3 st +3
. . .
t +3
a
Slide credit: Sutton & Barto
37
39. Designing a Learning System
• Choose the training experience
• Choose exactly what is to 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
Based on slide by Ray Mooney
Training data
Testing data
41
40. Training vs. Test Distribution
• We generally assume that the training and
test examples are independently drawn from
the same overall distribution of data
– We call this “i.i.d” which stands for “independent
and identically distributed”
• If examples are not independent, requires
collective classification
• If test distribution is different, requires
transfer learning
Slide credit: Ray Mooney
42
41. ML in a Nutshell
• Tens of thousands of machine learning
algorithms
– Hundreds new every year
• Every ML algorithm has three components:
– Representation
– Optimization
– Evaluation
Slide credit: Pedro Domingos
43
42. 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
Slide credit: Ray Mooney
44
44. Evaluation
• Accuracy
• Precision and recall
• Squared error
• Likelihood
• Posterior probability
• Cost / Utility
• Margin
• Entropy
• K-L divergence
• etc.
Slide credit: Pedro Domingos
47
45. ML in Practice
• Understand domain, prior knowledge, and goals
• Data integration, selection, cleaning, pre-processing, etc.
• Learn models
• Interpret results
• Consolidate and deploy discovered knowledge
Based on a slide by Pedro Domingos
Loop
48
46. 49
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.
Slide credit: Ray Mooney
48. 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
Slide credit: Ray Mooney
51
49. 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
Slide credit: Ray Mooney
52
50. 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)
– E-mail management
– Personalized assistants that learn
– Learning in robotics and vision
• 2010s
– Deep learning systems
– Learning for big data
– Bayesian methods
– Multi-task & lifelong learning
– Applications to vision, speech, social networks, learning to read, etc.
– ???
Based on slide by Ray Mooney
53
51. What We’ll Cover in this Course
• Supervised learning
– Decision tree induction
– Linear regression
– Logistic regression
– Support vector machines
& kernel methods
– Model ensembles
– Bayesian learning
– Neural networks & deep
learning
– Learning theory
• Unsupervised learning
– Clustering
– Dimensionality reduction
• Reinforcement learning
– Temporal difference
learning
– Q learning
• Evaluation
• Applications
Our focus will be on applying machine learning to real applications
54