- 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
Valencian Summer School 2015
Day 1
Lecture 3
Decision Trees
Gonzalo Martínez (UAM)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
Valencian Summer School 2015
Day 1
Lecture 3
Ensembles of Decision Trees
Gonzalo Martínez (UAM)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
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.
Valencian Summer School 2015
Day 1
Lecture 3
Decision Trees
Gonzalo Martínez (UAM)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
Valencian Summer School 2015
Day 1
Lecture 3
Ensembles of Decision Trees
Gonzalo Martínez (UAM)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
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.
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 Machine Learning Algorithms presentation will help you learn you what machine learning is, and the various ways in which you can use machine learning to solve a problem. At the end, you will see a demo on linear regression, logistic regression, decision tree and random forest. This Machine Learning Algorithms presentation is designed for beginners to make them understand how to implement the different Machine Learning Algorithms.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. Real world applications of Machine Learning
2. What is Machine Learning?
3. Processes involved in Machine Learning
4. Type of Machine Learning Algorithms
5. Popular Algorithms with a hands-on demo
- Linear regression
- Logistic regression
- Decision tree and Random forest
- N Nearest neighbor
What is 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.
- - - - - - - -
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
- - - - - - -
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
What is the Covering (Rule-based) algorithm?
Classification Rules- Straightforward
1. If-Then rule
2. Generating rules from Decision Tree
Rule-based Algorithm
1. The 1R Algorithm / Learn One Rule
2. The PRISM Algorithm
3. Other Algorithm
Application of Covering algorithm
Discussion on e/m-learning application
Basic of Decision Tree Learning. This slide includes definition of decision tree, basic example, basic construction of a decision tree, mathlab example
Automatic Attendace using convolutional neural network Face Recognitionvatsal199567
Automatic Attendance System will recognize the face of the student through the camera in the class and mark the attendance. It was built in Python with Machine Learning.
Artificial Intelligence Course: Linear models ananth
In this presentation we present the linear models: Regression and Classification. We illustrate with several examples. Concepts such as underfitting (Bias) and overfitting (Variance) are presented. Linear models can be used as stand alone classifiers for simple cases and they are essential building blocks as a part of larger deep learning networks
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 Machine Learning Algorithms presentation will help you learn you what machine learning is, and the various ways in which you can use machine learning to solve a problem. At the end, you will see a demo on linear regression, logistic regression, decision tree and random forest. This Machine Learning Algorithms presentation is designed for beginners to make them understand how to implement the different Machine Learning Algorithms.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. Real world applications of Machine Learning
2. What is Machine Learning?
3. Processes involved in Machine Learning
4. Type of Machine Learning Algorithms
5. Popular Algorithms with a hands-on demo
- Linear regression
- Logistic regression
- Decision tree and Random forest
- N Nearest neighbor
What is 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.
- - - - - - - -
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
- - - - - - -
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
What is the Covering (Rule-based) algorithm?
Classification Rules- Straightforward
1. If-Then rule
2. Generating rules from Decision Tree
Rule-based Algorithm
1. The 1R Algorithm / Learn One Rule
2. The PRISM Algorithm
3. Other Algorithm
Application of Covering algorithm
Discussion on e/m-learning application
Basic of Decision Tree Learning. This slide includes definition of decision tree, basic example, basic construction of a decision tree, mathlab example
Automatic Attendace using convolutional neural network Face Recognitionvatsal199567
Automatic Attendance System will recognize the face of the student through the camera in the class and mark the attendance. It was built in Python with Machine Learning.
Artificial Intelligence Course: Linear models ananth
In this presentation we present the linear models: Regression and Classification. We illustrate with several examples. Concepts such as underfitting (Bias) and overfitting (Variance) are presented. Linear models can be used as stand alone classifiers for simple cases and they are essential building blocks as a part of larger deep learning networks
Aaron Roth, Associate Professor, University of Pennsylvania, at MLconf NYC 2017MLconf
Aaron Roth is an Associate Professor of Computer and Information Sciences at the University of Pennsylvania, affiliated with the Warren Center for Network and Data Science, and co-director of the Networked and Social Systems Engineering (NETS) program. Previously, he received his PhD from Carnegie Mellon University and spent a year as a postdoctoral researcher at Microsoft Research New England. He is the recipient of a Presidential Early Career Award for Scientists and Engineers (PECASE) awarded by President Obama in 2016, an Alfred P. Sloan Research Fellowship, an NSF CAREER award, and a Yahoo! ACE award. His research focuses on the algorithmic foundations of data privacy, algorithmic fairness, game theory and mechanism design, learning theory, and the intersections of these topics. Together with Cynthia Dwork, he is the author of the book “The Algorithmic Foundations of Differential Privacy.”
Abstract Summary:
Differential Privacy and Machine Learning:
In this talk, we will give a friendly introduction to Differential Privacy, a rigorous methodology for analyzing data subject to provable privacy guarantees, that has recently been widely deployed in several settings. The talk will specifically focus on the relationship between differential privacy and machine learning, which is surprisingly rich. This includes both the ability to do machine learning subject to differential privacy, and tools arising from differential privacy that can be used to make learning more reliable and robust (even when privacy is not a concern).
Machine learning and linear regression programmingSoumya Mukherjee
Overview of AI and ML
Terminology awareness
Applications in real world
Use cases within Nokia
Types of Learning
Regression
Classification
Clustering
Linear Regression Single Variable with python
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Machine Learning Foundations for Professional ManagersAlbert Y. C. Chen
20180526@Taiwan AI Academy, Professional Managers Class.
Covering important concepts of classical machine learning, in preparation for deep learning topics to follow. Topics include regression (linear, polynomial, gaussian and sigmoid basis functions), dimension reduction (PCA, LDA, ISOMAP), clustering (K-means, GMM, Mean-Shift, DBSCAN, Spectral Clustering), classification (Naive Bayes, Logistic Regression, SVM, kNN, Decision Tree, Classifier Ensembles, Bagging, Boosting, Adaboost) and Semi-Supervised learning techniques. Emphasis on sampling, probability, curse of dimensionality, decision theory and classifier generalizability.
Machine learning is a powerful tool with many well-suited applications for malware detection, classification, and risk quantification. Despite its reputation as a "black box" component to an enterprise security solution, designing a robust machine learning model for malware detection is an involved process: its success hinges on understanding the problem you're trying to solve, the underlying data you utilize, and most importantly, its limitations.
In this Malware Most Wanted session, we analyze working models discuss the strengths, pitfalls, and high-level trade-offs of using machine learning for successful malware detection.
This slide gives brief overview of supervised, unsupervised and reinforcement learning. Algorithms discussed are Naive Bayes, K nearest neighbour, SVM,decision tree, Markov model.
Difference between regression and classification. difference between supervised and reinforcement, iterative functioning of Markov model and machine learning applications.
Covering important topics of Classical Machine Learning in 16 hours, in preparation for the following 10 weeks of Deep Learning courses at Taiwan AI academy from 2018/02-2018/05. Topics include regression (linear, polynomial, gaussian and sigmoid basis functions), dimension reduction (PCA, LDA, ISOMAP), clustering (K-means, GMM, Mean-Shift, DBSCAN, Spectral Clustering), classification (Naive Bayes, Logistic Regression, SVM, kNN, Decision Tree, Classifier Ensembles, Bagging, Boosting, Adaboost) and Semi-Supervised learning techniques. Emphasis on sampling, probability, curse of dimensionality, decision theory and classifier generalizability.
Surface features with nonparametric machine learningSylvain Ferrandiz
For data savvy users (analysts, scientists, ops, engineers) who are willing to discover some nonparametric machine learning algos that might help while competing via Kaggle or, more down-to-earth-ly, while having not that much time to spend on some predictive analytics projects. Talk given at Paris Kaggle meetup.
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.
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
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.
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.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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.
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.
3. TO LIVE IS TO PREDICT
Biology
- Food: edible or
poisonous?
- Fight/flight/freeze
- Position within the
hierarchy
- Mating choice
- Financial markets
- Betting markets
- Economic forecasts
- Business plans
- Election results
- Sports betting
Forecasting
Example 3- Career/job choice
- Moving
- Medical
interventions
- How to compete
with machines?
Modern life
• Effective decision-making requires accurate predictions.
• Humans and other species have evolved adaptations to cope with uncertainty:
• Memory: data storage linked to the ability to generate predictions
• Mental time traveling: ability to project oneself into the future
• Automated processes associated with certain emotions
4. 16,000 words
spoken per person
per day
100 trillion words
words spoken by
humanity per day
28 million papers
(1980-2012)
130 million books
indexed by Google
1 billion websites
on the World Wide Web
500 million videos
hosted on YouTube
BIG TEXT DATA
5. COMPETITION AS A DISCOVERY PROCESS
• Machine learning is organized around competitions.
• A dataset is split up into two parts:
• a training set and a test set
• Competitors submit models trained on the first set.
• Open source ensures perfect replicability.
• Models are then evaluated based on the second set.
• Competition winners tend to dominate the discourse …
• … until the next improvement is published.
• “State of the art”: best competition performances
6. DOMAIN-GENERAL LEARNING: COMPUTER VISION 1/2
• Domain-general learning strategies
• Example: convolutional neural networks
Rapid progress in image classification
• LeCun et al. (1998): a CNN trained on the MNIST dataset (60,000 small images of digits, 9 classes) achieves an error
rate of 1-2% when tested on 10,000 images
• Krizhevsky et al. (2012): a deep CNN trained on 1.2 million high-resolution images and 1,000 classes achieves a 17%
top-5 error rate when tested on 150,000 images
Skin cancer classification
• Esteva et al. (2016): Deep CNN, trained on 130,000 clinical images
• Human-level performance when tested against 21 dermatologists on two binary classification tasks
• karitinocytes carcinomas: most common skin cancer
• melanomas: deadliest skin cancer
7. DOMAIN GENERALITY: COMPUTER VISION 2/2
• Is there something special about skin cancer classification?
• Litjens et al. (2017) summarizes the use of deep learning for medical image analysis:
• at least 300 papers, most published in 2016
• CNNs have already been applied in the 90s:
• Lo et al. (1995): CNN trained to recognize lung nodules in x-rays
• In most cases, the only input to the learning algorithms is a set of pairs.
• Each pair consists of an image and a label:
• Malignant vs. benign
• Stage 1/2/3/4
8. DOMAIN GENERALITY: NATURAL LANGUAGE PROCESSING
Text classification
• Kim (2014): a shallow CNN outperforms state-of-the-art (SOTA) results in sentence classification tasks
• Conneau et al. (2017): very deep CNNs improve upon SOTA results in short text classification tasks
Sequence labeling
• Map a sequence of words to a sequence of tags:
• Strubell et al. (2017): A new CNN variant achieves almost SOTA results, but 10-20X faster
Machine translation
• Kalchbrenner et al. (2017): SOTA performance on an English-German translation benchmark
Tim Cook is Chief Executive Officer of Apple .
B-Name I-Name O B-Title I-Title I-Title O B-Org O
10. The basic workflow in a machine learning project:
WORKFLOW
IMPROVE
(when needed)
Error analysis,
more data,
“better” models
PROBLEM
FORMULATION
Can you describe the
problem in terms of
existing solutions?
DATA COLLECTION
How can you obtain
a large high-quality
dataset?
MODEL TRAINING
& SELECTION
What is a good
model? How do
you measure
success?
11. PROBLEM FORMULATION
TYPE OUTPUT COMMENT APPLICATIONS
Multi-class
classification
can be thought of as a special case of
sequence prediction
probability
distribution
topic classification, very good to very
bad, object classification, staging
Binary classification probability a special case of multi-class classification
yes/no, positive/negative,
present/absent, similar/dissimilar
Sequence prediction
sequence of
probability
distributions
sequence labeling, machine translation,
speech synthesis, image segmentation
at the core of intelligence
(artificial and biological)
Clustering
segmentation: customers, images
detection: communities, anomalies
cluster
membership
another special case:
number of clusters is a hyperparameter
robotics, driverless cars, conversational
agents, game playing
sequence predictions in an active
environment: actions effect observations
sequence of
actions
Reinforcement
learning
12. DATA COLLECTION
• A data set D is a collection of n data points di.
• Each data point di = (xi, ti) in D is a pair consisting of features x and a target t.
• Easy problems require, at least, hundreds or thousands of data points.
• Harder problems require millions of data points.
• Occasionally, data will be provided or is available in existing databases.
• Usually, a data collection strategy has to be devised and implemented.
Weakly supervised Example 3
• Humans manually label
the inputs with
appropriate targets.
• “This is a cat. That’s a
dog. This is another cat.”
• Minimalhumanintervention
• Downloadallimagesw/
thehashtags#cat,#dog
• Syntheticdata
• Nohumansupervision
• Learnpotentially
relevantpatternsfrom
giganticdatasets
Supervision Unsupervised
13. WHAT IS A MODEL, ANYWAY?
• Using parameters θ, a model f generates a prediction y from an input x:
• f(x, θ) = y
• Parameters allow the model to “weight the evidence”.
• Example: a simple binary classification problem
• Does a given article from a news archive focus on politics? Yes or no?
• Consider the parameters (weights) for the following words:
• Which of these parameters will be positive and negative?
election soccerthe
15. Logistic loss
LOSS FUNCTION: LOGISTIC LOSS
The target can be either 1 (“did occur”) or 0 (“did not occur”).
If the target equals 1: -log(prediction)
If the target equals 0: -log(1-prediction)
loss (prediction, target) = target log (prediction)-
[
(1 - target) log (1 - prediction)+ ]
Loss
- Some predictions are better than others.
- The deviation of the prediction p from the target t is referred to as loss.
- Synonyms: cost, error, empirical risk
- The loss is calculated through a loss function.
- Logistic loss is one of the most important loss functions.
16. LOGISTIC LOSS: EXAMPLES
Good prediction Mediocre predictionBad predictionBad prediction
loss prediction, target = −[target log(prediction) + 1 − target log(1 − prediction)]
Loss: -log(0.9) ≈ 0.046
This is a good prediction.
Consequently, the loss is
small.
Target: 1
Prediction: 90%
Target: 1
Prediction: 10%
Loss: -log(0.1) ≈ 0.699
This prediction is
inaccurate and the loss,
therefore, is high.
Target: 0
Prediction: 40%
Loss: -log (1-0.4) ≈ 0.222
This loss is a function of
the counter-probability of
60%.
17. WHY LOGISTIC LOSS?
• The likelihood function returns the probability of the data for a given parameter.
• In practice, it is convenient to use the log likelihood:
log L parameters data = log
i=1
n
P data pointi parameter) =
i=1
n
logP(data pointi|parameter)
L parameters data = P data parameter =
i=1
n
P data pointi parameter
• Coin flip example: L(ph=0.5|HT) = P(HT|ph=0.5) = 0.25
Likelihood
Log likelihood
• Using the log likelihood helps prevent underflow problems.
18. MAXIMUM LIKELIHOOD PRINCIPLE
The maximum likelihood principle tells us to select the parameters θ∗ that maximize the probability of the data:
A maximization problem w.r.t. to f(x) is equivalent to a minimization problem w.r.t. to f(-x):
For a random variable with two outcomes, the logistic loss is the negative log likelihood.
Thus, minimizing the logistic loss is equivalent to the maximum likelihood approach.
θ∗= arg maxθ
i=1
n
log P(data pointi|θ)
θ∗= arg minθ[−
i=1
n
log P(data pointi|θ) ]
20. NUMBER COMPLETION TASK
3, 9, 27, 81, ?
What is the next number in this sequence?
Simple solution
f(x)=3x
f(1) = 3, f(2) = 9, f(3)= 27, f(4) = 81
f(5) = 243
f(x)= -15 + 32x – 18x2 + 4x3
f(1) = 3, f(2) = 9, f(3) = 27, f(4) = 81
But: f(5) = 195
More complex solution
21. SCOTUS’S RAZOR
• Problem: There is an infinite number of solutions to any
sequence prediction problem.
• Most, if not all, machine learning problems are
sequence prediction problems.
• One solution: Ockham’s Razor
• Prefer the simplest theory consistent with the data
• First clear formulation by 13th century theologian
Duns Scotus
• Today: Don’t use a fancy machine learning model
when a simple model works just fine.
22. WHY OCKHAM’S RAZOR?
• It works.
• Successful applications in in ML, science, business, design and other fields
• Fast & cheap
• Simpler models tend to be faster models and consume fewer resources.
• The Schmidhuber/Hutter argument:
• The Great Programmer implements all possible universes with program lengths from 1 to N.
• Program B is a functional copy of program A if both lead to the same result but with different code.
• Simpler programs have more functional copies than longer programs.
• Simple program: print(“Hello world!”)
• Functional copy: const message = “Hello world!”; print(message)
24. • A neuron is the basic processing unit:
• Accepts input, processes input, sends
output
• Neurons are connected to other
neurons.
• Connections are weighted.
• A layer is a group of neurons.
• Every neural network has an input layer
and an output layer.
• Hidden layer:
• Any layer between input and output
• Shallow: ~ 1-5 hidden layers
• Deep: dozens or hundreds of layers
BUILDING BLOCKS: NEURONS, WEIGHTS AND LAYERS
Output:
f(w1x1+...+w3x3)
Input layer Output layer
Input 1: x1
Input 2: x2
...
Input n: xn
w1
w2
wn
26. • Baseline model for binary
classification tasks:
• f(x) = s(w1x1+...+wnxn+b)
• Weigh the evidence
• Add a bias
• The sigmoid function s “squashes” the
input to a number b/w 0 and 1.
• Can be formulated as a neural net:
• The input x1, ..., xn corresponds to
neurons in the first layer.
• The bias corresponds to an additional
neuron with a connection weight of 1.
• The output neuron applies the sigmoid
function.
LOGISTIC REGRESSION
Input layer Output layer
Bias: b
Input 1: x1
Input 2: x2
Input n: xn
Output:
s(w1x1+...+wnxn+b)
...
w1
wn
w2
1
27. THE SIGMOID FUNCTION
The sigmoid function s(x) is one of the most
frequently used functions in machine learning:
𝑠 𝑥 =
1
1 + 𝑒−𝑥
Desirable properties:
• “squashes” any input into the range between 0 and 1
• The derivative is easy to compute:
𝑑𝑠
𝑑𝑥
= 𝑠 𝑥 (1 − 𝑠 𝑥 )
28. A GLIMPSE AT BACKPROPAGATION
• Model parameters are initialized randomly.
• The term “training” refers to the (iterative) optimization of parameters.
• Almost all neural nets are trained with the backpropagation algorithm.
Backpropagation algorithm (n repetitions)
Go through each instance:
1. Forward propagation: Compute the prediction and the loss.
2. Backward propagation: For each parameter, compute the derivative w.r.t. the loss.
• Positive derivative: small increase in parameter => increase in loss
• Negative derivative: small increase in parameter => decrease in loss
3. Update: Use the derivative to apply an update rule.
• Simple rule: old value = new value – learning rate derivative
39. CONVOLUTIONAL LAYER
• CNNs use a repeated sequence of layers:
• A convolutional layer, followed by a pooling layer
• A convolutional layer consists of filters:
• A window moves through the output of the
previous layer
• Similar to how we read: from left to right, and
then downwards
• The purpose of a filter is to detect the
presence of a particular feature:
• Basic geometric shapes
• Lines, circles, edges
• Characteristic colors
• Blue sky, green grass
40. 96 low-level features learned by a convolution layer
Source: CS231n Convolutional Neural Networks for Visual Recognition
41. MAX-POOLING LAYER
• A max-pooling layer performs a
reduction operation:
• A window moves through the subregions of
the previous output.
• For each subregion, the maximum value is
extracted.
• A max-pooling layer with a stride of 2
reduces a 4x4 matrix to a 2x2 matrix.
• Intuition: It doesn’t really matter where
exactly a feature is located.
• Less entries => faster computation
42. DEEP CONVOLUTIONAL NEURAL NETWORKS
• Deep neural nets are characterized by repeated blocks of layers.
• Ex.: a series of convolution/max-pooling operations
• Some ML fields (though not all) are dominated by deep nets.
• Theory lags behind applications.
• Intuition: hierarchical models for hierarchical data
• Simple example: traffic sign recognitions
• Lines and circles form digits.
• Digits form numbers.
• A speed limit sign is composed of a red circle,
a white circle and a number.
44. SUMMARY
• The growth of machine learning is fueled by:
1. the importance of predictions
2. the low cost of data acquisition and processing
3. the domain generality of learning algorithms.
• The essential task in machine learning is to
1. formulate the problem
2. collect a large and relevant the dataset
3. train, test and improve appropriate models.
• Neural networks form a class of powerful models trained by backpropagation:
• Building blocks: neurons, connections, layer
• Convolutional neural networks:
• high predictive accuracy and computational efficiency