This document introduces machine learning concepts through a webinar presentation. It begins with introductions and definitions of machine learning from Wikipedia and O'Reilly. It then provides examples of artificial intelligence and machine learning applications. The main machine learning concepts covered include supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is described as learning from labeled examples, while unsupervised learning finds patterns in unlabeled data. Reinforcement learning involves an agent interacting with an environment and receiving rewards or punishments to achieve goals. Examples of reinforcement learning applications include autonomous vehicles and game playing agents. In closing, the presenter thanks college administrators and attendees for their participation.
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters.
Below topics are explained in this Machine Learning presentation:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Machine Learning Algorithms
- Linear Regression
- Decision Trees
- Support Vector Machine
5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
This knolx is about an introduction to machine learning, wherein we see the basics of various different algorithms. This knolx isn't a complete intro to ML but can be a good starting point for anyone who wants to start in ML. In the end, we will take a look at the demo wherein we will analyze the FIFA dataset going through the understanding of various data analysis techniques and use an ML algorithm to derive 5 players that are similar to each other.
Machine learning(ML) is the scientific study of algorithms and statistical models that computer systems used to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “Training Data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in the applications of email filtering, detection of network intruders and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, Machine learning is the study of computer systems that learn from data and experience. It is applied in an incredibly wide variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to make sense of data is a potential customer of machine learning.
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters.
Below topics are explained in this Machine Learning presentation:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Machine Learning Algorithms
- Linear Regression
- Decision Trees
- Support Vector Machine
5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
This knolx is about an introduction to machine learning, wherein we see the basics of various different algorithms. This knolx isn't a complete intro to ML but can be a good starting point for anyone who wants to start in ML. In the end, we will take a look at the demo wherein we will analyze the FIFA dataset going through the understanding of various data analysis techniques and use an ML algorithm to derive 5 players that are similar to each other.
Machine learning(ML) is the scientific study of algorithms and statistical models that computer systems used to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “Training Data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in the applications of email filtering, detection of network intruders and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, Machine learning is the study of computer systems that learn from data and experience. It is applied in an incredibly wide variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to make sense of data is a potential customer of machine learning.
Machine Learning. What is machine learning. Normal computer vs ML. Types of Machine Learning. Some ML Object detection methods. Faster CNN, RCNN, YOLO, SSD. Real Life ML Applications. Best Programming Languages for ML. Difference Between Machine Learning And Artificial Intelligence. Advantages of Machine Learning. Disadvantages of Machine Learning
A PPT which gives a brief introduction on Machine Learning and on the products developed by using Machine Learning Algorithms in them. Gives the introduction by using content and also by using a few images in the slides as part of the explanation. It includes some examples of cool products like Google Cloud Platform, Cozmo (a tiny robot built by using Artificial Intelligence), IBM Watson and many more.
The world today is evolving and so are the needs and requirements of people. Furthermore, we are witnessing a fourth industrial revolution of data.
Machine Learning has revolutionized industries like medicine, healthcare, manufacturing, banking, and several other industries. Therefore, Machine Learning has become an essential part of modern industry.
This was part of my inaugural lecture of Summer Internship on Machine Learning at NMAM Institute of Technology, Nitte on 7th June, 2018. A lot more than what was on this presentation was discussed. We spoke on the ethics of choices we make as developers, socio-cultural impact of AI and ML and the political repercussions of deploying ML and AI.
Machine Learning. What is machine learning. Normal computer vs ML. Types of Machine Learning. Some ML Object detection methods. Faster CNN, RCNN, YOLO, SSD. Real Life ML Applications. Best Programming Languages for ML. Difference Between Machine Learning And Artificial Intelligence. Advantages of Machine Learning. Disadvantages of Machine Learning
A PPT which gives a brief introduction on Machine Learning and on the products developed by using Machine Learning Algorithms in them. Gives the introduction by using content and also by using a few images in the slides as part of the explanation. It includes some examples of cool products like Google Cloud Platform, Cozmo (a tiny robot built by using Artificial Intelligence), IBM Watson and many more.
The world today is evolving and so are the needs and requirements of people. Furthermore, we are witnessing a fourth industrial revolution of data.
Machine Learning has revolutionized industries like medicine, healthcare, manufacturing, banking, and several other industries. Therefore, Machine Learning has become an essential part of modern industry.
This was part of my inaugural lecture of Summer Internship on Machine Learning at NMAM Institute of Technology, Nitte on 7th June, 2018. A lot more than what was on this presentation was discussed. We spoke on the ethics of choices we make as developers, socio-cultural impact of AI and ML and the political repercussions of deploying ML and AI.
what-is-machine-learning-and-its-importance-in-todays-world.pdfTemok IT Services
Machine Learning is an AI method for teaching computers to learn from their mistakes. Machine learning algorithms can “learn” data directly from data without using an equation as a model by employing computational methods.
https://bit.ly/RightContactDataSpecialists
Machine learning is a technology design to build intelligent systems. These systems also have the ability to learn from past experience or analyze historical data. It provides results according to its experience.
Alpavdin defines Machine Learning as-
“Optimizing a performance criterion using example data and past experience”.
Data is the key concept of machine learning. We can also apply its algorithms on data to identify hidden patterns and gain insights. These patterns and gained knowledge help systems to learn and improve their performance.
Machine learning technology involves both statistics and computer science. Statistics allows one to draw inferences from the given data. To implement efficient algorithms we can also use computer science. It represents the required model, and evaluate the performance of the model.
Machine Learning is a fascinating field that has been making headlines for its incredible advancements in recent years. Whether you're a tech enthusiast or just curious about how machines can learn, this article will provide you with a simple and easy-to-understand overview of some key Machine Learning concepts. Think of it as your first step towards a Machine Learning Complete Course!
Machine Learning Interview Questions and AnswersSatyam Jaiswal
Practice Best Machine Learning Interview Questions and Answers for the best preparation of the machine learning interview. these questions are very popular and asked various times in machine learning interview.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
Introduction to Machine Learning
1.
2.
3. I’m Sujith Jayaprakash, Currently working as
the Director and Busienss Head of NewEdge
InfoTech Gh Ltd. I’m a business development
professional with a strong background in IT
Training and administration. I have more than
a decade of experience in the education
sectors in India, Africa and Latin America with
significant experience in Senior Management
roles and leading institutional academic
delivery improvement. I have completed
Doctor of Philosophy in Computer Science
and specialized in Education Data Mining.
Area of Expertise : Data Science, Web Mining,
Education Data Mining, and Social Media Marketing
HELLO!
4. This webinar is solely focused on introducing you to
machine learning concepts, Hence most of the topics
I deal here would be just an introduction to various ML
applications and steps involved.
So if you’re already have a fair bit of experience in ML
or Data Science then this webinar might not be of your
interest.
Cheers! Let’s Start
5. WHAT IS MACHINE LEARNING
Wikipedia
Machine learning (ML) is the study of
computer algorithms that improve
automatically through experience.
O’Reilly
ML is a subset of the larger field of artificial
intelligence (AI) that “focuses on teaching
computers how to learn without the need to
be programmed for specific tasks - Sujit
Pal and Antonio Gulli in Deep Learning with
Keras.
01
02
Some
common
definitions
from reliable
sources
7. AI Vs. ML Vs. NN Vs. DL Vs. DS
AI is for building models that
emulate cognition and human
understanding. AI is the
implementation of a predictive
model to forecast future events
Data Science
Data Science is about finding
hidden patterns in the data.
Data Science comprises of
various statistical techniques
whereas AI makes use of
computer algorithms
Deep Learning
Deep learning is a subset of
machine learning in artificial
intelligence (AI) that has networks
capable of learning unsupervised
from data that is unstructured or
unlabeled.
Artifical Intelligence
Neural Network
A neural network is a series
of algorithms that endeavors
to recognize underlying
relationships in a set of data
through a process that
mimics the way the human
brain operates.
Machine Learning
Machine learning is an application
of artificial intelligence (AI) that
provides systems the ability to
automatically learn and improve
from experience without being
explicitly programmed.
8. Real Life Examples of AI & Machine Learning
Automated Cars & Google Map Face Detection Video Surveillance
Product Recommendation Sentimental Analysis AI in Agriculture
9. Machine Leanring Concepts
Machine learning helps you
to use historical data to
make better business
decisions
ML models are used to
predict the future data
based on the historical
data
ML algorithms discover patterns
and data and construct
mathematical models using those
discoveries.
02
03
01
10. Learning a Function
y – Output Variable
f – Target Function
x – Input variables
Machine learning algorithms are described as learning a target function (f)
that best maps input variables (X) to an output variable (Y).
Y = f(x)
11. Product Recommendation Scenario
Classic Approach ML Approach
“ Assuming you are tasked with developing a
back end application that provides product
recommendation to customers based on past
purchases.
Recommendation
Purchase History
Creating Rules
Model
Purchase History
Site wide
Customer Access
Sales Data
Most recent
purchases
Recommended
12. Simple Algorithm
f(x) = a0x0 + a1x1 + a2x2 . . . . anxn
Feature: An Important Data Point
x0: Is the product a shirt?
Yes = 1
Weight: How much does the feature affect the
accuracy of the prediction?
a0: This customer has purchased 8 shirts in the
past
Weight = 0.8
x1: Is this item from a Brand [y]?
Yes = 1
Weight: How much does the feature affect the
accuracy of the prediction?
a1: 2/8 items this person bought in the past
were brand [y].
Weight = 0.25
If f(x) > 1, recommend the product
f(x) = 0.8 * 1 + 0.25 * 1
f(x) = 1.05
Two Key Components of the algorithm
Features : Are part of the dataset that are
identified as important to identify the
outcome
Weights : To determine how important the
associated feature is.
14. 1. Supervised learning is the machine learning task of learning a function that maps an input to an output based
on example input-output pairs. It infers a function from labeled training data consisting of a set of training
examples.
2. With supervised learning, you feed the output of your algorithm into the system. This means that in supervised
learning, the machine already knows the output of the algorithm before it starts working on it or learning it. A
basic example of this concept would be a student learning a course from an instructor. The student knows what
he/she is learning from the course.
3. With the output of the algorithm known, all that a system needs to do is to work out the steps or process
needed to reach from the input to the output. The algorithm is being taught through a training data set that
guides the machine. If the process goes haywire and the algorithms come up with results completely different
than what should be expected, then the training data does its part to guide the algorithm back towards the right
path.
4. Supervised Machine Learning currently makes up most of the ML that is being used by systems across the
world. The input variable (x) is used to connect with the output variable (y) through the use of an algorithm. All
of the input, the output, the algorithm, and the scenario are being provided by humans. We can understand
supervised learning in an even better way by looking at it through two types of problems.
Supervised Learning
15. Supervised Learning
Supervised Learning
Classification Regression
Classification problems categorize all the variables that
form the output. Examples of these categories formed
through classification would include demographic data such
as marital status, sex, or age. The most common model
used for this type of service status is the support vector
machine. The support vector machines set forth to define
the linear decision boundaries.
Problems that can be classified as regression problems
include types where the output variables are set as a real
number. The format for this problem often follows a linear
format.
CLASSIFICATIONREGRESSION
16. 1. Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets
consisting of input data without labeled responses.
2. The most common unsupervised learning method is cluster analysis, which is used for exploratory data
analysis to find hidden patterns or grouping in data. The clusters are modeled using a measure of similarity
which is defined upon metrics such as Euclidean or probabilistic distance.
3. Unsupervised learning algorithms allows you to perform more complex processing tasks compared to
supervised learning. Although, unsupervised learning can be more unpredictable compared with other natural
learning methods..
4. Unsupervised learning methods are used in bioinformatics for sequence analysis and genetic clustering; in
data mining for sequence and pattern mining; in medical imaging for image segmentation; and in computer
vision for object recognition.
Unsupervised Learning
17. Unsupervised Learning
Unsupervised Learning
Dimensionality
Reduction
Clustering
Dimensionality reduction or dimension reduction is the
process of reducing the number of random variables under
consideration by obtaining a set of principal variables.
Approaches can be divided into feature selection and
feature extraction.
Clustering can be considered the most important unsupervised learning problem; so,
as every other problem of this kind, it deals with finding a structure in a collection of
unlabeled data. A loose definition of clustering could be “the process of organizing
objects into groups whose members are similar in some way”. A cluster is therefore
a collection of objects which are “similar” between them and are “dissimilar” to the
objects belonging to other clusters.
DIMENSIONALITY
REDUCTION
CLUSTERING
18.
19. 1. A system interacts with a dynamic environment in which it must perform a certain goal (such as driving a
vehicle or playing a game against an opponent).
2. The system is provided feedback in terms of rewards and punishments as it navigates its problem space.
3. Reinforcement learning can be understood using the concepts of agents, environments, states, actions and
rewards, all of which we’ll explain below.
Reinforcement Learning
20. 1. For example, in usual circumstances we would require an autonomous vehicle to put safety first, minimize ride
time, reduce pollution, offer passengers comfort and obey the rules of law. With an autonomous race car, on
the other hand, we would emphasize speed much more than the driver’s comfort. The programmer cannot
predict everything that could happen on the road. Instead of building lengthy “if-then” instructions, the
programmer prepares the reinforcement learning agent to be capable of learning from the system of rewards
and penalties. The agent (another name for reinforcement learning algorithms performing the task) gets
rewards for reaching specific goals.
Reinforcement Learning Examples
21.
22.
23.
24.
25.
26. 2
6
Dr. K. Prem Nazeer, Principal
(Dr. S. N. S Rajalakshmi College of Arts and Science)
Dr. V. Kathiresan, Director
(Dr. S. N. S Rajalakshmi College of Arts and Science)
Dr. N. Shanmugapriya, HOD
(Dr. S. N. S Rajalakshmi College of Arts and Science)
Entire SNS Team
& You all