- 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.
Supervised vs Unsupervised vs Reinforcement Learning | EdurekaEdureka!
YouTube: https://youtu.be/xtOg44r6dsE
(** Python Data Science Training: https://www.edureka.co/python **)
In this PPT on Supervised vs Unsupervised vs Reinforcement learning, we’ll be discussing the types of machine learning and we’ll differentiate them based on a few key parameters. The following topics are covered in this session:
1. Introduction to Machine Learning
2. Types of Machine Learning
3. Supervised vs Unsupervised vs Reinforcement learning
4. Use Cases
Python Training Playlist: https://goo.gl/Na1p9G
Python Blog Series: https://bit.ly/2RVzcVE
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
Index.....................
History of Machine Learning.
What is Machine Learning.
Why ML.
Learning System Model.
Training and Testing.
Performance.
Algorithms.
Machine Learning Structure.
Application.
Conclusion.
----------------------------------------------
THANK YOU
Supervised vs Unsupervised vs Reinforcement Learning | EdurekaEdureka!
YouTube: https://youtu.be/xtOg44r6dsE
(** Python Data Science Training: https://www.edureka.co/python **)
In this PPT on Supervised vs Unsupervised vs Reinforcement learning, we’ll be discussing the types of machine learning and we’ll differentiate them based on a few key parameters. The following topics are covered in this session:
1. Introduction to Machine Learning
2. Types of Machine Learning
3. Supervised vs Unsupervised vs Reinforcement learning
4. Use Cases
Python Training Playlist: https://goo.gl/Na1p9G
Python Blog Series: https://bit.ly/2RVzcVE
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
Index.....................
History of Machine Learning.
What is Machine Learning.
Why ML.
Learning System Model.
Training and Testing.
Performance.
Algorithms.
Machine Learning Structure.
Application.
Conclusion.
----------------------------------------------
THANK YOU
Lecture1 introduction to machine learningUmmeSalmaM1
Machine Learning is a field of computer science which deals with the study of computer algorithms that improve automatically through experience. In this PPT we discuss the following concepts - Prerequisite, Definition, Introduction to Machine Learning (ML), Fields associated with ML, Need for ML, Difference between Artificial Intelligence, Machine Learning, Deep Learning, Types of learning in ML, Applications of ML, Limitations of Machine Learning.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
** Machine Learning Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka Machine Learning PPT on "Complete Machine Learning Course" will provide you with detailed and comprehensive knowledge of Machine Learning. It will provide you with the in-depth knowledge of the different types of Machine Learning with the different algorithms that lie under each category with a demo for each algorithm and the approach one should take to solve these problems. This PPT will be covering the following topics:
What is Data Science?
Data Science Peripherals
What is Machine learning?
Features of Machine Learning
How it works?
Applications of Machine Learning
Market Trend of Machine Learning
Machine Learning Life Cycle
Important Python Libraries
Types of Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Detailed Supervised Learning
Supervised Learning Algorithms
Linear Regression
Use Case(with Demo)
Model Fitting
Need for Logistic Regression
What is Logistic Regression?
What is Decision Tree?
What is Random Forest?
What is Naïve Bayes?
Detailed Unsupervised Learning
What is Clustering?
Types of Clustering
Market Basket Analysis
Association Rule Mining
Example
Apriori Algorithm
Detailed Reinforcement Learning
Reward Maximization
The Epsilon Greedy Algorithm
Markov Decision Process
Q-Learning
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
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.
Lecture1 introduction to machine learningUmmeSalmaM1
Machine Learning is a field of computer science which deals with the study of computer algorithms that improve automatically through experience. In this PPT we discuss the following concepts - Prerequisite, Definition, Introduction to Machine Learning (ML), Fields associated with ML, Need for ML, Difference between Artificial Intelligence, Machine Learning, Deep Learning, Types of learning in ML, Applications of ML, Limitations of Machine Learning.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
** Machine Learning Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka Machine Learning PPT on "Complete Machine Learning Course" will provide you with detailed and comprehensive knowledge of Machine Learning. It will provide you with the in-depth knowledge of the different types of Machine Learning with the different algorithms that lie under each category with a demo for each algorithm and the approach one should take to solve these problems. This PPT will be covering the following topics:
What is Data Science?
Data Science Peripherals
What is Machine learning?
Features of Machine Learning
How it works?
Applications of Machine Learning
Market Trend of Machine Learning
Machine Learning Life Cycle
Important Python Libraries
Types of Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Detailed Supervised Learning
Supervised Learning Algorithms
Linear Regression
Use Case(with Demo)
Model Fitting
Need for Logistic Regression
What is Logistic Regression?
What is Decision Tree?
What is Random Forest?
What is Naïve Bayes?
Detailed Unsupervised Learning
What is Clustering?
Types of Clustering
Market Basket Analysis
Association Rule Mining
Example
Apriori Algorithm
Detailed Reinforcement Learning
Reward Maximization
The Epsilon Greedy Algorithm
Markov Decision Process
Q-Learning
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
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 presentation discusses decision trees as a machine learning technique. This introduces the problem with several examples: cricket player selection, medical C-Section diagnosis and Mobile Phone price predictor. It discusses the ID3 algorithm and discusses how the decision tree is induced. The definition and use of the concepts such as Entropy, Information Gain are discussed.
Generative Adversarial Networks and Their ApplicationsArtifacia
This is the presentation from our AI Meet Jan 2017 on GANs and its applications.
You can join Artifacia AI Meet Bangalore Group: https://www.meetup.com/Artifacia-AI-Meet/
Generative Adversarial Networks is an advanced topic and requires a prior basic understanding of CNNs. Here is some pre-reading material for you.
- https://arxiv.org/pdf/1406.2661v1.pdf
- https://arxiv.org/pdf/1701.00160v1.pdf
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
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!
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
3. Lecture No. 1
Ravi Gupta
AU-KBC Research Centre,
MIT Campus, Anna University
Date: 5.03.2008
4. What is Machine Learning ???
Machine Learning (ML) is concerned with
the question of how to construct computer
programs that automatically improves with
experience.
5. When Machine Learning ???
• Computers applied for solving a complex problem
• No known method for computing output is present
• When computation is expensive
6. When Machine Learning ???
• Classification of protein types based amino acid sequences
• Screening of credit applicants (who will default and who will
not)
• Modeling a complex chemical reaction, when the precise
interaction of the different reactants is unknown
19. Definition (Learning)
A computer program is said to learn from
experience E with respect to some class of tasks
T and performance measure P, if its performance
at tasks in T, as measured by P, improves with
experience E. (Tom Mitchell, 1998)
20. Checker Learning Problem
A computer program that learns to play checkers might
improve its performance as measured by its ability to win at the
class of tasks involving playing checkers games, through
experience obtained by playing games against itself
• Task T : playing checkers
• Performance measure P: % of game won against opponents
• Training experience E : playing practice game against itself
21. A Handwritten Recognition
Problem
• Task T : recognizing and classifying handwritten words
within images
• Performance measure P: % of words correctly classified
• Training experience E : a database of handwritten
words with given classifications
22. A Robot Driving Learning Problem
• Task T : driving on public four-lane highways using
vision sensors
Performance measure P: average distance traveled
before an error (as judged by human overseer)
• Training experience E : a sequence of images and
steering commands recorded while observing a human
driver
24. Checker Learning Problem
• Task T : playing checkers
• Performance measure P: % of game won against
opponents
• Training experience E : playing practice game
against itself
26. Choosing the Training Experience
The type of training experience E available to a system
can have significant impact on success or failure of the
learning system.
27. Choosing the Training Experience
One key attribute is whether the training experience
provides direct or indirect feedback regarding the
choices made by the performance system
30. Second attribute is the degree to which the learner
controls the sequence of training examples.
Next
Move
31. Another attribute is the degree to which the learner
controls the sequence of training examples.
Teacher / Expert
Player Next Move
Player Next Move
32. How well the training experience represents the
distribution of examples over which the final system
performance P must be measured.
(Learning will be most reliable when the training
example follow a distribution similar to that of future
test examples)
36. Geri’s Game by Pixar in which an elderly man enjoys a game of chess
with himself (available at http://www.pixar.com/shorts/gg/theater/index.html)
Won an Academy Award for best animated short film.
37. Assumptions
• Let us assume that our system will train by
playing games against itself.
• And it is allowed to generate as much training
data as time permits.
38. Issues Related to Experience
What type of
knowledge/experience
should one learn??
How to represent the
experience ? (some
mathematical
representation for
experience)
What should be the
learning mechanism???
40. Target Function
Choose Move: B → M
Choose Move is a function
where input B is the set of legal board states
and produces M which is the set of legal moves
45. Representation of Target Function
• xl: the number of white pieces on the board
• x2: the number of red pieces on the board
• x3: the number of white kings on the board
• x4: the number of red kings on the board
• x5: the number of white pieces threatened by red (i.e.,
which can be captured on red's next turn)
• X6: the number of red pieces threatened by white
F'(b) = w0 + w1x1+ w2x2 + w3x3 + w4x4 + w5x5 + w6x6
46. • Task T: playing checkers
• Performance measure P: % of games won in the world
tournament
• Training experience E: games played against itself
• Target function: F : Board → R
• Target function representation
F'(b) = w0 + w1x1+ w2x2 + w3x3 + w4x4 + w5x5 + w6x6
The problem of learning a checkers strategy reduces to the
problem of learning values for the coefficients w0 through w6 in
the target function representation
48. Generic issues of Machine Learning
• What algorithms exist for learning general target functions from
specific training examples?
• In what settings will particular algorithms converge to the desired
function, given sufficient training data?
• Which algorithms perform best for which types of problems and
representations?
• How much training data is sufficient?
• What is the best way to reduce the learning task to one or more
function approximation problems?
49. Generic issues of Machine Learning
• When and how can prior knowledge held by the learner guide the
process of generalizing from examples?
• Can prior knowledge be helpful even when it is only approximately
correct?
• What is the best strategy for choosing a useful next training
experience, and how does the choice of this strategy alter the
complexity of the learning problem?
• How can the learner automatically alter its representation to
improve its ability to represent and learn the target function?
51. Concept Learning
A task of acquiring a potential hypothesis (solution) that
best fits the training examples
52. Concept Learning Task
Objective is to learn EnjoySport
{Sky, AirTemp, Humidity, Wind, Water, Forecast} → EnjoySport
Tom enjoys his favorite water sports
56. Concept Learning
Acquiring the definition of a general category from a given set of
positive and negative training examples of the category.
instance
A hypothesis h in H such that h(x) = c(x) for all x in X
hypothesis Target Instance
concept
57. Instance Space
Suppose that the target hypothesis that we want to
learn for the current problem is represented as a
conjunction of all the attributes
Sky = .... AND AirTemp = …. AND Humidity= …. AND Wind = …. AND
Water= …. AND Forecast= …. THEN EnjoySport= ….
58. Instance Space
Suppose the attribute Sky has three possible values,
and that AirTemp, Humidity, Wind, Water, and
Forecast each have two possible values
Instance
Space
Possible distinct instances = 3 * 2 * 2 * 2 * 2 * 2 = 96
59. Hypothesis Space
Hypothesis Space: A set of all possible hypotheses
Possible syntactically distinct Hypotheses for EnjoySport
= 5 * 4 * 4 * 4 * 4 * 4 = 5120
• Sky has three possible values
• Fourth value don’t care (?)
• Fifth value is empty set Ø
62. Concept Learning as Search
Concept learning can be viewed as the task of searching through
a large space of hypothesis implicitly defined by the hypothesis
representation.
The goal of the concept learning search is to find the hypothesis
that best fits the training examples.
63. General-to-Specific Learning
Every day Tom his
enjoy i.e., Only
positive examples.
Most General Hypothesis: h = <?, ?, ?, ?, ?, ?>
Most Specific Hypothesis: h = < Ø, Ø, Ø, Ø, Ø, Ø>
64. General-to-Specific Learning
Consider the sets of instances that are classified positive by hl and by h2. h2 imposes
fewer constraints on the instance, thus it classifies more instances as positive.
Any instance classified positive by hl will also be classified positive by
h2. Therefore, we say that h2 is more general than hl.
65. Definition
Given hypotheses hj and hk, hj is more_general_than_or_equal_to
hk if and only if any instance that satisfies hk also satisfies hj.
We can also say that hj is more_specific_than hk when hk is
more_general_than hj.
74. Key Properties of FIND-S Algorithm
• Guaranteed to output the most specific hypothesis within H
that is consistent with the positive training examples.
• Final hypothesis will also be consistent with the negative
examples provided the correct target concept is contained in
H, and provided the training examples are correct.
75. Unanswered Questions by FIND-S
• Has the learner converged to the correct
target concept?
Although FIND-S will find a hypothesis consistent with the training data, it
has no way to determine whether it has found the only hypothesis in H
consistent with the data (i.e., the correct target concept
• Why prefer the most specific hypothesis?
In case there are multiple hypotheses consistent with the training examples,
FIND-S will find the most specific. It is unclear whether we should prefer this
hypothesis over, say, the most general, or some other hypothesis of
intermediate generality.
76. Unanswered Questions by FIND-S
• Are the training examples consistent?
In most practical learning problems there is some chance that the training
examples will contain at least some errors or noise. Such inconsistent sets
of training examples can severely mislead FIND-S, given the fact that it
ignores negative examples. We would prefer an algorithm that could at least
detect when the training data is inconsistent and, preferably, accommodate
such errors.
77. Unanswered Questions by FIND-S
• What if there are several maximally specific
consistent hypotheses?
In the hypothesis language H for the EnjoySport task, there is always a
unique, most specific hypothesis consistent with any set of positive
examples. However, for other hypothesis spaces (which will be discussed
later) there can be several maximally specific hypotheses consistent with
the data.