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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
Machine learning is the study of algorithms that improve performance on a task based on experience. It is used when human expertise does not exist, humans cannot explain their expertise, models must be customized to large amounts of data, or learning can help automation. The learning task is defined by the performance metric, task, and experience. Applications include face recognition, self-driving cars, spam detection, recommendations, and more. The main types of learning are supervised, unsupervised, semi-supervised, and reinforcement learning. Supervised learning uses training data and labels, unsupervised learning uses only training data, and reinforcement learning uses rewards from a sequence of actions.
This document summarizes a talk on practical machine learning issues. It discusses identifying the right machine learning scenario for a given task, such as classification, regression, clustering, or reinforcement learning. It also addresses common reasons why machine learning models may fail, such as using the wrong evaluation metrics, not having enough labeled training data, or not performing proper feature engineering. The document emphasizes the importance of choosing the appropriate machine learning model, having sufficient high-quality data, and selecting useful features.
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.
This document provides an introduction to machine learning. It discusses the history of machine learning, including early work in neural networks and decision trees. It defines machine learning as the ability to improve performance on tasks based on experience. The key components of a learning problem are identified as the task, data used for learning, and a performance measure. Linear regression, decision trees, instance-based learning, Bayesian learning, support vector machines, neural networks, and clustering are listed as machine learning algorithms. Designing a learning system involves choosing training experiences, a target function, representation of that function, and a learning algorithm.
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.
The document provides an introduction to machine learning including:
- A brief history of machine learning from the 1950s to present day.
- Definitions of machine learning and the components of a learning problem.
- Overviews of different types of machine learning including supervised, unsupervised, semi-supervised, and reinforcement learning.
- Descriptions of common machine learning tasks like classification and regression.
- Explanations of key machine learning concepts such as hypothesis space, inductive bias, and model performance.
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.
Lecture 09(introduction to machine learning)Jeet Das
Machine learning allows computers to learn without explicit programming by analyzing data to recognize patterns and make predictions. It can be supervised, learning from labeled examples to classify new data, or unsupervised, discovering hidden patterns in unlabeled data through clustering. Key aspects include feature representation, distance metrics to compare examples, and evaluation methods like measuring error on test data to avoid overfitting to the training data.
This document provides an introduction and overview of machine learning. It discusses different types of machine learning including supervised, unsupervised, semi-supervised and reinforcement learning. It also covers key machine learning concepts like hypothesis space, inductive bias, representations, features, and more. The document provides examples to illustrate these concepts in domains like medical diagnosis, entity recognition, and image recognition.
This document summarizes a presentation about machine learning and predictive analytics. It discusses formal definitions of machine learning, the differences between supervised and unsupervised learning, examples of machine learning applications, and evaluation metrics for predictive models like lift, sensitivity, and accuracy. Key machine learning algorithms mentioned include logistic regression and different types of modeling. The presentation provides an overview of concepts in machine learning and predictive analytics.
The document discusses key concepts related to process management in operating systems. It describes that an OS executes programs as processes, which can be in various states like running, waiting, ready etc. It also explains process control blocks that contain details of a process like state, registers, scheduling info etc. The document discusses process scheduling and synchronization techniques used by the OS to share CPU and other resources between multiple processes. It describes mechanisms for process creation, termination and interprocess communication using shared memory and message passing.
This document provides an introduction to operating systems. It discusses what an operating system is, its key functions such as process management, memory management, file management, device management, and security. It describes the evolution of operating systems from early batch systems to modern multiprogramming, time-sharing, and distributed systems. Popular types of operating systems are also outlined, including desktop, server, mobile, and embedded operating systems. Key topics like kernels, system calls, computer architecture, and user interfaces are summarized as well.
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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
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.
Machine learning is the study of algorithms that improve performance on a task based on experience. It is used when human expertise does not exist, humans cannot explain their expertise, models must be customized to large amounts of data, or learning can help automation. The learning task is defined by the performance metric, task, and experience. Applications include face recognition, self-driving cars, spam detection, recommendations, and more. The main types of learning are supervised, unsupervised, semi-supervised, and reinforcement learning. Supervised learning uses training data and labels, unsupervised learning uses only training data, and reinforcement learning uses rewards from a sequence of actions.
This document summarizes a talk on practical machine learning issues. It discusses identifying the right machine learning scenario for a given task, such as classification, regression, clustering, or reinforcement learning. It also addresses common reasons why machine learning models may fail, such as using the wrong evaluation metrics, not having enough labeled training data, or not performing proper feature engineering. The document emphasizes the importance of choosing the appropriate machine learning model, having sufficient high-quality data, and selecting useful features.
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This document provides an introduction to machine learning. It discusses the history of machine learning, including early work in neural networks and decision trees. It defines machine learning as the ability to improve performance on tasks based on experience. The key components of a learning problem are identified as the task, data used for learning, and a performance measure. Linear regression, decision trees, instance-based learning, Bayesian learning, support vector machines, neural networks, and clustering are listed as machine learning algorithms. Designing a learning system involves choosing training experiences, a target function, representation of that function, and a learning algorithm.
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The document provides an introduction to machine learning including:
- A brief history of machine learning from the 1950s to present day.
- Definitions of machine learning and the components of a learning problem.
- Overviews of different types of machine learning including supervised, unsupervised, semi-supervised, and reinforcement learning.
- Descriptions of common machine learning tasks like classification and regression.
- Explanations of key machine learning concepts such as hypothesis space, inductive bias, and model performance.
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Difference between regression and classification. difference between supervised and reinforcement, iterative functioning of Markov model and machine learning applications.
Lecture 09(introduction to machine learning)Jeet Das
Machine learning allows computers to learn without explicit programming by analyzing data to recognize patterns and make predictions. It can be supervised, learning from labeled examples to classify new data, or unsupervised, discovering hidden patterns in unlabeled data through clustering. Key aspects include feature representation, distance metrics to compare examples, and evaluation methods like measuring error on test data to avoid overfitting to the training data.
This document provides an introduction and overview of machine learning. It discusses different types of machine learning including supervised, unsupervised, semi-supervised and reinforcement learning. It also covers key machine learning concepts like hypothesis space, inductive bias, representations, features, and more. The document provides examples to illustrate these concepts in domains like medical diagnosis, entity recognition, and image recognition.
This document summarizes a presentation about machine learning and predictive analytics. It discusses formal definitions of machine learning, the differences between supervised and unsupervised learning, examples of machine learning applications, and evaluation metrics for predictive models like lift, sensitivity, and accuracy. Key machine learning algorithms mentioned include logistic regression and different types of modeling. The presentation provides an overview of concepts in machine learning and predictive analytics.
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How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
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
6
Slide credit: Geoffrey Hinton
6. 7
Slide credit: Geoffrey Hinton
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
7. 8
Slide credit: Pedro Domingos
SAMPLE APPLICATIONS
• Web search
• Computational biology
• Finance
• E-commerce
• Space exploration
• Robotics
• Information extraction
• Social networks
• Debugging software
• [Your favorite area]
8. DEFINING THE LEARNING TASK
T:Improve on task
P:with respect to performance metric
E:based on experience
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.
Slide credit: Ray Mooney
10
9. STATE OF THE ART APPLICATIONS OF
MACHINE LEARNING
11
10. AUTONOMOUS CARS
• 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
Penn’s Autonomous Car
(Ben Franklin Racing Team) 12
12. AUTONOMOUS CAR TECHNOLOGY
Laser Terrain Mapping
Stanley
Learning from Human Drivers
Sebastian
Adaptive Vision
Path
Planning
Images and movies taken from Sebastian Thrun’s multimedia w1e4bsite.
16. 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.
18
Slide credit: Andrew Ng
17. SCENE LABELING VIA DEEP LEARNING
[Farabet et al. ICML 2012, PAMI 2013] 19
18. 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
19. 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
20. IMPACT OF DEEP LEARNING IN SPEECH
TECHNOLOGY
Slide credit: Li Deng, MS Research
22
22. 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
23. MACHINE LEARNING TYPES
Ml types
Supervised
Continuous Target
Variables
Regression
(like House price
Prediction)
Categorical Target
Variables
Classification(like
Medical Imaging)
Unsupervised
Target values not
known
Clustering (like
Customer
Segmentation)
Association (like
Market Basket
Analysis)
Semi-supervised
Categorical Target
Available
Classification (like
Text
Classification)
Clustering (like
Lane finding using
GPS)
Reinforcement
Categorical Target
Variable
Classification (like
Optimized
Marketing)
Target variable not
available
Control (like
Driverless cars)
25. 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
9
8
7
6
5
4
3
2
1
0
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
26. SUPERVISED LEARNING: CLASSIFICATION
28
• Given (x1, y1), (x2, y2), ..., (xn, yn)
• Learn a function f (x) to predict y given x
– y is categorical == classification
Breast Cancer (Malignant / Benign)
1(Malignant)
0(Benign)
Tumor Size
Tumor Size
Based on example by Andrew Ng
27. SUPERVISED LEARNING: CLASSIFICATION
29
• Given (x1, y1), (x2, y2), ..., (xn, yn)
• Learn a function f (x) to predict y given x
– y is categorical == classification
Breast Cancer (Malignant / Benign)
1(Malignant)
0(Benign)
Tumor Size
Predict Benign Predict Malignant
Tumor Size
Based on example by Andrew Ng
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
30. 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
31. UNSUPERVISED LEARNING
34
• 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
32. UNSUPERVISED LEARNING
35
• 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
33. REINFORCEMENT LEARNING
• Given a sequence of states and actions with
(delayed) rewards, output a policy
– Policy is a mapping from states 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
34. THE AGENT-ENVIRONMENT INTERFACE
Agent and environment interact at discrete time steps
Agent observes state at step t: st S
: t 0, 1, 2, K
produces action at step t : at A(st )
gets resulting reward :
and resulting next state :
rt1
st1
. . . st at
rt +1 st +1
at +1
rt +2 st +2
at +2
rt +3 st +3
. . .
at +3
Slide credit: Sutton & Barto
37
38. 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
39. 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
40. 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
41. VARIOUS FUNCTION REPRESENTATIONS
44
Slide credit: Ray Mooney
• 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. 47
Slide credit: Pedro Domingos
EVALUATION
• Accuracy
• Precision and recall
• Squared error
• Likelihood
• Posterior probability
• Cost / Utility
• Margin
• Entropy
• K-L divergence
• etc.
44. 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
Loop
48
Based on a slide by Pedro Domingos
45. 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