This document provides an overview of machine learning techniques for classification and anomaly detection. It begins with an introduction to machine learning and common tasks like classification, clustering, and anomaly detection. Basic classification techniques are then discussed, including probabilistic classifiers like Naive Bayes, decision trees, instance-based learning like k-nearest neighbors, and linear classifiers like logistic regression. The document provides examples and comparisons of these different methods. It concludes by discussing anomaly detection and how it differs from classification problems, noting challenges like having few positive examples of anomalies.
Binary Class and Multi Class Strategies for Machine LearningPaxcel Technologies
This presentation discusses the following -
Possible strategies to follow when working on a new machine learning problem.
The common problems with classifiers (how to detect them and eliminate them).
Popular approaches on how to use binary classifiers to problems with multi class classification.
Binary Class and Multi Class Strategies for Machine LearningPaxcel Technologies
This presentation discusses the following -
Possible strategies to follow when working on a new machine learning problem.
The common problems with classifiers (how to detect them and eliminate them).
Popular approaches on how to use binary classifiers to problems with multi class classification.
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
In this presentation, we approach a two-class classification problem. We try to find a plane that separates the class in the feature space, also called a hyperplane. If we can't find a hyperplane, then we can be creative in two ways: 1) We soften what we mean by separate, and 2) We enrich and enlarge the featured space so that separation is possible.
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Simplilearn
This Support Vector Machine (SVM) presentation will help you understand Support Vector Machine algorithm, a supervised machine learning algorithm which can be used for both classification and regression problems. This SVM presentation will help you learn where and when to use SVM algorithm, how does the algorithm work, what are hyperplanes and support vectors in SVM, how distance margin helps in optimizing the hyperplane, kernel functions in SVM for data transformation and advantages of SVM algorithm. At the end, we will also implement Support Vector Machine algorithm in Python to differentiate crocodiles from alligators for a given dataset.
Below topics are explained in this Support Vector Machine presentation:
1. What is Machine Learning?
2. Why support vector machine?
3. What is support vector machine?
4. Understanding support vector machine
5. Advantages of support vector machine
6. Use case in Python
- - - - - - - -
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.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
In machine learning, support vector machines (SVMs, also support-vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.
This presentation briefly defines machine learning and its types of algorithms. After that two algorithms are presented. The first is naive bayes classifier for text classification and later k-means for clustering including some strategies to improve results.
Analysis of data is an important task in data managements systems. Many mathematical tools are used in data analysis. A new division of data management has appeared in machine learning, linear algebra, an optimal tool to analyse and manipulate the data. Data science is a multi-disciplinary subject that uses scientific methods to process the structured and unstructured data to extract the knowledge by applying suitable algorithms and systems. The strength of linear algebra is ignored by the researchers due to the poor understanding. It powers major areas of Data Science including the hot fields of Natural Language Processing and Computer Vision. The data science enthusiasts finding the programming languages for data science are easy to analyze the big data rather than using mathematical tools like linear algebra. Linear algebra is a must-know subject in data science. It will open up possibilities of working and manipulating data. In this paper, some applications of Linear Algebra in Data Science are explained.
In this tutorial, we will learn the the following topics -
+ Linear SVM Classification
+ Soft Margin Classification
+ Nonlinear SVM Classification
+ Polynomial Kernel
+ Adding Similarity Features
+ Gaussian RBF Kernel
+ Computational Complexity
+ SVM Regression
In this tutorial, we will learn the the following topics -
+ Voting Classifiers
+ Bagging and Pasting
+ Random Patches and Random Subspaces
+ Random Forests
+ Boosting
+ Stacking
Data Science - Part IX - Support Vector MachineDerek Kane
This lecture provides an overview of Support Vector Machines in a more relatable and accessible manner. We will go through some methods of calibration and diagnostics of SVM and then apply the technique to accurately detect breast cancer within a dataset.
Aaa ped-14-Ensemble Learning: About Ensemble LearningAminaRepo
In this section we will start talking effectively about ensemble learning. We will simply talk about the different methods that exist to combine different models. We will then implement those methods in Python.
[Notebook](https://colab.research.google.com/drive/1fNkOh7iQ_AnjNWxm3hWyR4DIGRUNwzsS)
Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand what is clustering, K-Means clustering, flowchart to understand K-Means clustering along with demo showing clustering of cars into brands, what is logistic regression, logistic regression curve, sigmoid function and a demo on how to classify a tumor as malignant or benign based on its features. Machine Learning algorithms can help computers play chess, perform surgeries, and get smarter and more personal. K-Means & logistic regression are two widely used Machine learning algorithms which we are going to discuss in this video. Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps to predict the probability of an event by fitting data to a logit function. It is also called logit regression. K-means clustering is an unsupervised learning algorithm. In this case, you don't have labeled data unlike in supervised learning. You have a set of data that you want to group into and you want to put them into clusters, which means objects that are similar in nature and similar in characteristics need to be put together. This is what k-means clustering is all about. Now, let us get started and understand K-Means clustering & logistic regression in detail.
Below topics are explained in this Machine Learning tutorial part -2 :
1. Clustering
- What is clustering?
- K-Means clustering
- Flowchart to understand K-Means clustering
- Demo - Clustering of cars based on brands
2. Logistic regression
- What is logistic regression?
- Logistic regression curve & Sigmoid function
- Demo - Classify a tumor as malignant or benign based on features
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.
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/
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimentional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side.
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
In this presentation, we approach a two-class classification problem. We try to find a plane that separates the class in the feature space, also called a hyperplane. If we can't find a hyperplane, then we can be creative in two ways: 1) We soften what we mean by separate, and 2) We enrich and enlarge the featured space so that separation is possible.
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Simplilearn
This Support Vector Machine (SVM) presentation will help you understand Support Vector Machine algorithm, a supervised machine learning algorithm which can be used for both classification and regression problems. This SVM presentation will help you learn where and when to use SVM algorithm, how does the algorithm work, what are hyperplanes and support vectors in SVM, how distance margin helps in optimizing the hyperplane, kernel functions in SVM for data transformation and advantages of SVM algorithm. At the end, we will also implement Support Vector Machine algorithm in Python to differentiate crocodiles from alligators for a given dataset.
Below topics are explained in this Support Vector Machine presentation:
1. What is Machine Learning?
2. Why support vector machine?
3. What is support vector machine?
4. Understanding support vector machine
5. Advantages of support vector machine
6. Use case in Python
- - - - - - - -
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.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
In machine learning, support vector machines (SVMs, also support-vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.
This presentation briefly defines machine learning and its types of algorithms. After that two algorithms are presented. The first is naive bayes classifier for text classification and later k-means for clustering including some strategies to improve results.
Analysis of data is an important task in data managements systems. Many mathematical tools are used in data analysis. A new division of data management has appeared in machine learning, linear algebra, an optimal tool to analyse and manipulate the data. Data science is a multi-disciplinary subject that uses scientific methods to process the structured and unstructured data to extract the knowledge by applying suitable algorithms and systems. The strength of linear algebra is ignored by the researchers due to the poor understanding. It powers major areas of Data Science including the hot fields of Natural Language Processing and Computer Vision. The data science enthusiasts finding the programming languages for data science are easy to analyze the big data rather than using mathematical tools like linear algebra. Linear algebra is a must-know subject in data science. It will open up possibilities of working and manipulating data. In this paper, some applications of Linear Algebra in Data Science are explained.
In this tutorial, we will learn the the following topics -
+ Linear SVM Classification
+ Soft Margin Classification
+ Nonlinear SVM Classification
+ Polynomial Kernel
+ Adding Similarity Features
+ Gaussian RBF Kernel
+ Computational Complexity
+ SVM Regression
In this tutorial, we will learn the the following topics -
+ Voting Classifiers
+ Bagging and Pasting
+ Random Patches and Random Subspaces
+ Random Forests
+ Boosting
+ Stacking
Data Science - Part IX - Support Vector MachineDerek Kane
This lecture provides an overview of Support Vector Machines in a more relatable and accessible manner. We will go through some methods of calibration and diagnostics of SVM and then apply the technique to accurately detect breast cancer within a dataset.
Aaa ped-14-Ensemble Learning: About Ensemble LearningAminaRepo
In this section we will start talking effectively about ensemble learning. We will simply talk about the different methods that exist to combine different models. We will then implement those methods in Python.
[Notebook](https://colab.research.google.com/drive/1fNkOh7iQ_AnjNWxm3hWyR4DIGRUNwzsS)
Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand what is clustering, K-Means clustering, flowchart to understand K-Means clustering along with demo showing clustering of cars into brands, what is logistic regression, logistic regression curve, sigmoid function and a demo on how to classify a tumor as malignant or benign based on its features. Machine Learning algorithms can help computers play chess, perform surgeries, and get smarter and more personal. K-Means & logistic regression are two widely used Machine learning algorithms which we are going to discuss in this video. Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps to predict the probability of an event by fitting data to a logit function. It is also called logit regression. K-means clustering is an unsupervised learning algorithm. In this case, you don't have labeled data unlike in supervised learning. You have a set of data that you want to group into and you want to put them into clusters, which means objects that are similar in nature and similar in characteristics need to be put together. This is what k-means clustering is all about. Now, let us get started and understand K-Means clustering & logistic regression in detail.
Below topics are explained in this Machine Learning tutorial part -2 :
1. Clustering
- What is clustering?
- K-Means clustering
- Flowchart to understand K-Means clustering
- Demo - Clustering of cars based on brands
2. Logistic regression
- What is logistic regression?
- Logistic regression curve & Sigmoid function
- Demo - Classify a tumor as malignant or benign based on features
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.
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/
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimentional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side.
Sogang University Machine Learning and Data Mining lab seminar, Neural Networks for newbies and Convolutional Neural Networks. This is prerequisite material to understand deep convolutional architecture.
사내 스터디용으로 공부하며 만든 발표 자료입니다. 부족한 부분이 있을 수도 있으니 알려주시면 정정하도록 하겠습니다.
*슬라이드 6에 나오는 classical CNN architecture(뒤에도 계속 나옴)에서 ReLU - Pool - ReLu에서 뒤에 나오는 ReLU는 잘못된 표현입니다. ReLU - Pool에서 ReLU 계산을 또 하는 건 redundant 하기 때문입니다(Kyung Mo Kweon 피드백 감사합니다)
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
IMAGE CLASSIFICATION USING DIFFERENT CLASSICAL APPROACHESVikash Kumar
IMAGE CLASSIFICATION USING KNN, RANDOM FOREST AND SVM ALGORITHM ON GLAUCOMA DATASETS AND EXPLAIN THE ACCURACY, SENSITIVITY, AND SPECIFICITY OF EACH AND EVERY ALGORITHMS
- A high-level overview of artificial intelligence
- The importance of predictions across different domains of life
- Big (text) data
- Competition as a discovery process
- Domain-general learning
- Computer vision and natural language processing
- Elements of a machine learning system
- A hierarchy of problem classes
- Data collection
- The purpose of a model
- Logistic loss function
- Likelihood, log likelihood and maximum likelihood
- Ockham's Razor
- Intelligence as sequence prediction
- Building blocks of neural networks: neurons, weights and layers
- Logistic regression as a neural network
- Sigmoid function
- A look at backpropagation
- Gradient descent
- Convolutional neural networks
- Max-pooling
- Deep neural networks
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...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.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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.
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.
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.
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.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Introduction to conventional machine learning techniques
1. 1
BDIGITAL: After Work Knowledge Program
Practical approach to machine learning techniques for
classification and anomaly detection
Xavier Rafael-Palou
xrafael@bdigital.org
(12/12/2014)
4. 4
(Classic test)
Natural Language Processing - communication
Knowledge representation - knowledge storage (KS)
Automated reasoning - use KS to answer questions
Machine Learning - detect patterns, adapt (total Turing Test)
(Advanced Turing Test)
Computer vision - perceive objects
Robotics - manipulate objects + move around
Blade Runner (Ridley Scott, 1982): Deckard and the Voight-Kampff machine in 2019.
Inspired on Philip K. Dick's book "Do Android's Dream of Electric Sheep” (1968)
(*) Source:
“Artificial
Intelligence, a
modern approach“
by Stuart Russel &
Peter Norvig.
6. 6
Introduction
Classification, Anomaly detection but also clustering, regression are examples of
Machine Learning (ML) tasks.
ML is a subfield of Artificial Intelligence to :
- Give computers the ability to learn without being explicitly programmed. (Arthur
Samuel, 1959)
- Give computer program ability to learn from experience E with respect to some task
T and some performance measure P, if its performance on T, as measured by P,
improves with experience E. (Tom Mitchell, 1998)
Data mining (DM) overlaps in many ways with Machine Learning:
- DM uses many ML methods, but often with a slightly different goal of discovering
previously unknown knowledge.
-While ML aims to perform accurately on new, unseen examples/tasks after having
experienced a learning data set.
7. 7
Main ML tasks:
Supervised learning. The goal is to learn a general rule given a set of examples
that maps inputs to outputs.
Others:
Unsupervised learning, no labels are given to discovering patterns in data.
Reinforcement learning, interaction with a dynamic environment in which it must
perform a certain goal without a teacher.
Semi-supervised learning, the teacher gives an incomplete training set with some of the
target outputs missing.
14. 14
There are multiple classification techniques:
- Probabilistic
- Decision Tree
- Linear
- Instance-based
- Genetic algorithms
- Fuzzy logic
- …
Each of them learns a decision function in a different way:
Basic Classification Methods
15. 15
Probabilistic classifiers
Example: “Automatic fruit classification”
- Random variable (y) says if fruit is M or A
- Looking at the conveyor belt during some time, we get probs of M, A (“a priori”
knowledge of the harvest) P(y=M), P(y=A) both sum up 1
- Classifier: M if p(y=M) >= p(y=A) else A enough?
CompacInVision 9000
16. 16
- We add new random variable x to the system for a better performance
x = size degree of the fruit [1,2,3…]
- So, we get probs of p(x) too
- Since x depends on the type of fruit, we get densities of x depending on the type of
fruit:
p(x| y=A) , p(x | y=M) “conditional probability densities”
How size affects our attitude regarding the type of fruit in question?
- p(y=A | x) = (p(x| y=A) P(y=A)) / p(x)
- P(y=M | x) = (p(x| y=M) P(y=M)) /p(x)
Naive Bayes: A if p(y=A | x) >= p(y=M | x) else M (probs “a posteriori”)
Probabilistic classifiers
17. 17
Pros:
- Simple to implement
- Fast to compute (e.g. fits in map & reduce paradigm)
- works surprisingly well
- Compatible with missing data
- Used in text mining Multinomial Naive Bayes
Cons:
- Unrealistic hypothesis: All features equally important and independent of another
given a class
- Dependencies among features (i.e. recall all have same power)
- Zero probs holds a veto over other ones
- Requires process all data
Probabilistic classifiers
18. 18
- Widely used because of the ease of understanding of the knowledge proposed
- Set of conditions (nodes) organized hierarchically
- Prediction: Apply a new unseen instance from root to leaves of the tree
Decision Tree Learning
19. 19
Tid Refund Marital
Status
Taxable
Income Cheat
1 Yes Single 125K No
2 No Married 100K No
3 No Single 70K No
4 Yes Married 120K No
5 No Divorced 95K Yes
6 No Married 60K No
7 Yes Divorced 220K No
8 No Single 85K Yes
9 No Married 75K No
10 No Single 90K Yes
10
Training
Greedy strategy. Split records based on an attribute test that optimizes certain criterion.
The tree is built recursively adding conditions until the leaves containing the same kind
elements
- Partitioning strategy: best attribute, best condition NP problem
- Determine when to stop
Don’t
Cheat
Refund
Don’t
Cheat
Don’t
Cheat
Yes No
Refund
Don’t
Cheat
Yes No
Marital
Status
Don’t
Cheat
Cheat
Single,
Divorced
Married
Taxable
Income
Don’t
Cheat
< 80K >= 80K
Refund
Don’t
Cheat
Yes No
Marital
Status
Don’t
Cheat
Cheat
Single,
Divorced
Married
Example:
Decision Tree Learning
20. 20
Partitioning strategy : Preferred aattribute's that generate disjoint sets (homogeneity)
Strategy examples :
∑−=
j
tjptGINI 2
)]|([1)(
Non-homogeneous,
High degree of impurity
Homogeneous,
Low degree of impurity
p( j | t) is the relative frequency of class j at node t
)|(max1)( tjPtError
j
−=
Decision Tree Learning
Measures homogeneity of a node. Used in CART, SLIQ, SPRINT
−= ∑
=
k
i
i
split
iEntropy
n
n
pEntropyGAIN 1
)()(
Measures misclassification error made by a node
Choose split that achieves most homogeneity reduction (e.g. ID3, C4.5)
21. 21
Based on the principle that the instances within a dataset will generally exist in
close proximity to other instances that have similar properties.
kNN (Cover and Hart, 1967) locates the k nearest instances to the query instance and
determines its class by identifying the single most frequent class label.
Instances can be considered as points
within an n-dimensional instance space
where each of the n-dimensions corresponds
to one of the n-features.
A distance metric must minimize the distance
between two similarly classified instances,
while maximizing the distance between instances
of different classes
Instance-Based Learning
22. 22
Distance metrics: Euclidean distance (*), Mahalanobis, Manhattam,…
To determine the class given the neighbour list, we can use e.g. majority voting or
weights according to distance (1/d2)
Instance-Based Learning
23. 23
Pros:
- Less computational cost during training (Lazy learning)
Cons:
- Slow classification
- Requires store large amounts of information
- Sensitive to the choice of the similarity method
- Unclear selection criteria K
Instance-Based Learning
24. 24
x1
Decision Boundary
1 2 3
1
2
3
Predicts “ “ when…
The idea is to get a function h (x) (parameters and attributes) to partition
data into desired output classes
x2
Probabilistic Statistical Classification
Principal objective is to find h(x) :
25. 25
Then we predict “ “ if
predict “ “ if
z
1
Expected values for h(x) are :
We need to transform h(x) to accommodate it to this behavior (Sigmoid function)
Logistic Regression :
Replace z for:
Probabilistic Statistical Classification
26. 26
How to choose parameters ? Those that minimize error (cost)
If y = 1
10
Cost function
The more our hypothesis is off from y, the
larger the cost function output. If our
hypothesis is equal to y, then our cost is 0
Logistic Regression
Gradient descendent Method to find local minimum cost
27. 27
Logistic vs SVM vs Neural Networks
N (features) is large Preferred using a logistic regression, or SVM without a kernel
(the "linear kernel")
N is small and M (instances) is intermediate Preferred using a SVM with a Gaussian
Kernel
N is small and M is large manually create/add more features , then use logistic
regression or SVM without a kernel.
Neural networks is likely to work well for any of these situations, but may be slower to
train.
Comparative Classification Methods
29. 29
Supervised Machine Learning: A Review of Classification Techniques
S. B. Kotsiantis. Informatica 31 (2007) 249–268
Comparative Classification Methods
30. 30
Anomaly Detection
Anomalous behavior's Classification
• Fraud detection
• Manufacturing (e.g. aircraft
engines)
• Monitoring machines in a data
center
• Email spam classification
• Weather prediction
(sunny/rainy/etc).
• Cancer classification
31. 31
Anomaly detection vs Classification
Very small number of positive
examples (y=1). (0-20 is common).
Large number of negative (y=0)
examples.
Many different “types” of anomalies.
Hard for any algorithm to learn from
positive examples what the anomalies
look like; future anomalies may look
nothing like any of the anomalous
examples we’ve seen so far.
Large number of positive and
negative examples.
Enough positive examples for
algorithm to get a sense of what
positive examples are like, future
positive examples likely to be similar
to ones in training set.
32. 32
Given a new example we want to know whether is abnormal/anomalous.
We define a "model" p(x) that says the probability the example is not anomalous.
We use a threshold ϵ (epsilon) as a dividing line so we can say which examples are
anomalous and which are not.
If our anomaly detector is flagging too many anomalous examples, then we need to
decrease our threshold ϵ
Anomaly Detection Methods
33. 33
The Gaussian Distribution is a familiar bell-shaped curve that can be described by a
function N(μ,σ2)
Mu, or μ, describes the centre of the curve, called the mean. The width of the curve is
described by sigma, or σ, called the standard deviation.
Parameter μ is the average of all the examples:
We can estimate σ2, with our familiar squared error formula:
Gaussian Distribution Method
34. 34
Given a training set of examples, {x(1),…,x(m)} where each example is a vector, x∈Rn.
An "independent assumption" on the values of the features inside training example x.
More compactly, the above expression can be written as follows:
Anomaly if p(x)<ϵ
Gaussian Distribution Method
35. 35
Fit model on training set
On a cross validation/test, predict x as:
Possible evaluation metrics:
- True positive, false positive, false negative, true negative
- Precision/Recall
- F1-score
Tricks:
- Choose features that might take on unusually large or small values in the event of
an anomaly
- Use cross validation set to choose sigma parameter
- Train only on normal data
- Test and validation: add anomalies (50% each)
Gaussian Distribution Method
36. 36
An extension of anomaly detection and may (or may not) catch more anomalies.
Instead of modelling p(x1),p(x2),… separately, we will model p(x) all in one go.
Parameters are : μ∈ Rn and Σ ∈ Rn×n
We can vary Σ for changes in shape, width, and orientation of the contours.
Changing μ will move the centre of the distribution.
Anomaly if p(x)<ϵ
Multivariate Gaussian Distribution
37. 37
One-class SVM
The multivariate Gaussian model can automatically capture correlations between
different features of x.
However, the original model is computationally cheaper (no matrix to invert) and it
performs well even with small training set size.
One-class SVM can be used for anomaly detection.
Could work better than multivariate when data does not follow a Gaussian distribution
39. 39
If classification performance is not what we expected, What to work on?
- Get more training examples?
- Try smaller sets of features?
- Try getting additional features?
- Try changing model?
- Try decreasing regularization?
- Try increasing regularization?
Guides & Tips Building Classifiers
40. 40
The attributes
petal width and
petal length provide a
moderate separation of
the Irish species
Data exploration
Manually examine the examples (in cross validation set) that your algorithm made errors on.
See if you spot any systematic trend in what type of examples it is making errors on.
Arrange good features for your classifier:
- Discrimination ability: Values significantly different for objects of different classes
- Reliability: Similar values for objects same class
- Independence: Attributes should be uncorrelated. Instead, combine them:
E.g. diameter and weight: diameter3 / weight (scale invariant)
41. 41
Bias-Variance Trade-Off
- Balance between capacity generalize classifier performance
- Plot learning curves to decide if more data, more features, etc. are likely to help.
42. 42
Start with a simple algorithm that you can implement quickly.
Implement and test it on your cross-validation data.
Split data in 3 different sets: Training + Validation + Test
Accuracy, percentage of correct predictions (SPAM or no) by all predictions
Precision, percentage of e-mails classified as SPAMs which truly are
Recall, percentage of e-mails classified as SPAMs over the total of
examples that are SPAM
How to compare precision/recall numbers?
FNTP
TP
TPRrecall
+
==
FPTP
TP
precision
+
=
Model Evaluation
F1 Score:
44. 44
Practice: Environment
0) Python:
Language interpreted dynamically-typed nature
Download:
- Python already installed:
pip install ipython or only dependencies "ipython[notebook]“
- Otherwise:
Anaconda (http://continuum.io/downloads) is a completely free Python distribution
(including for commercial use and redistribution). It includes over 195 of the most
popular python packages for science, math, engineering, data analysis.
$ Conda info
$ conda install <packageName>
$ conda update <packageName>
45. 45
Practice: Environment
1) Ipython:
Ipython provides a rich architecture for interactive computing with:
- Powerful interactive shells (terminal and Qt-based).
- A browser-based notebook with support for code, text, mathematical expressions,
inline plots and other rich media.
- Support for interactive data visualization and use of GUI toolkits.
- Flexible, embeddable interpreters to load into your own projects.
- Easy to use, high performance tools for parallel computing.
Start console Ipython –pylab
46. 46
Practice: Environment
2) Notebook
Web-based interactive computational environment where to combine code execution,
text, mathematics, plots and rich media into a single document
Start notebook server ipython notebook
(http://127.0.0.1:8888)
Open an existing notebook ipython notebook <name.ipynb>
The notebook consists of a sequence of cells.
A cell is a multi-line text input field, and its contents can be executed by commands or
clicking either “Play” button, or Cell | Run in the menu bar.
Commands:
Shift-Enter Runs cell and goes to next
Ctrl-Enter Runs cell & stays in same cell
Esc and Enter Command mode and edit mode
Tab auto-complete
47. 47
Practice: Environment
3) Numpy + scipy
Numpy offers a specific data structure for high-performance numerical computing:
the multidimensional array
- Data is stored in contiguous block of memoryin Ram. This makes more efficient
Use of cpu cycles and cache
- Array operations implemented internally with C loops rather than python.
Numpy has all standard array functions, linear algebra, and fancy indexing.
Numpy+scipy docs: http://docs.scipy.org
4) Matplotlib
Graphical library to plot and visualize your data
5) Scikit-Learn
Librería para machine learning
49. 49
Practice: Exercises
- An introduction to machine learning with Python and scikit-learn (repo and overview)
by Hannes Schulz and Andreas Mueller.
- PyCon 2014 Scikit-learn Tutorial (Ipython and machine learning) by Jake VanderPlas
51. 51
References
- Data mining. Practical Machine Learning Tools and Techniques. I. Frank, et al
- Introduction to Machine learning with Ipython. LxMLS 2014. A. Mueller
- Ipython and machine learning. PyCon ’14
- Introduction to Machine learning. Coursera 2014. A. Ng
- scikit-learn. http://scikit-learn.org (see especially the narrative documentation)
- Matplotlib. http://matplotlib.org (see especially the gallery section)
- Ipython. http://ipython.org (also check out http://nbviewer.ipython.org)
- Anaconda. https://store.continuum.io/cshop/anaconda/
- Notebook. http://ipython.org/ipython-doc/stable/notebook/index.html