This document outlines the course details for an Introduction to Machine Learning module. The module will cover the basics of machine learning including supervised learning techniques like classification and regression, as well as unsupervised learning techniques like clustering and dimensionality reduction. Students will learn to prepare data, visualize it, evaluate models, and communicate results to both technical and non-technical audiences. The course will involve lectures, labs, homework assignments, and a final written test. The goal is for students to understand the basics of machine learning and be able to apply the techniques to analyze real-world data.
This document provides an overview of an introduction to machine learning course, including:
- A description of the course content which covers Python programming, data visualization, supervised learning algorithms, regression, and unsupervised learning.
- An example of predicting bike share usage at different stations and the importance of understanding the problem and data.
- Guidance on exploring and visualizing data in Python to gain insights before applying machine learning algorithms.
Lessons learned from building practical deep learning systemsXavier Amatriain
1. There are many lessons to be learned from building practical deep learning systems, including choosing the right evaluation metrics, being thoughtful about your data and potential biases, and understanding dependencies between data, models, and systems.
2. It is important to optimize only what matters and beware of biases in your data. Simple models are often better than complex ones, and feature engineering is crucial.
3. Both supervised and unsupervised learning are important, and ensembles often perform best. Your AI infrastructure needs to support both experimentation and production.
Machine Learning for (DF)IR with Velociraptor: From Setting Expectations to a...Chris Hammerschmidt
achine Learning for DFIR with Velociraptor: From Setting Expectations to a Case Study
By Christian Hammerschmidt, PhD - Head of Engineering/ML, APTA Technologies
Machine learning (ML) or artificial intelligence (AI) often comes with great promise and large marketing budgets for cybersecurity, especially in monitoring (such as EDR/XDR solutions). Post-breach, it often turns out that the actual performance falls short of its promises.
In this talk, we’ll briefly look at ML for DFIR: What tasks can ML solve, generally speaking? What requirements do we have for a useful ML system in cybersecurity/DFIR contexts, such as reliability, robustness to attackers, and explainability? What makes ML difficult to apply in cybersecurity, e.g. when thinking about false alerts or attackers attempting to circumvent automated systems?
After discussing the basics, we look at ML for velociraptor:
How can we process forensic data collected with VQL using machine learning (with a typical Python/Jupyter/scikit-learn/PyTorch stack)?
And how can we build artifacts that run ML directly on each endpoint, avoiding central data collection?
The talk concludes with a case study, showing how we significantly reduced time to analyze EVTX files in incident response cases, saving thousands of USD in costs and reducing time to resolution.
Bio: Chris Hammerschmidt did his PhD research on machine learning methods for reverse engineering software systems. Now, he’s heading APTA Technologies, a start-up building machine learning tools to understand software behavior .
Affiliation: APTA Technologies, https://apta.tech
This document provides an overview of machine learning concepts covered in an Introduction to Machine Learning course. It discusses topics like binary and multiclass classification, evaluation metrics like precision and recall, imbalanced datasets, and algorithms like k-nearest neighbors, decision trees, support vector machines, and data projection techniques. Examples and illustrations are provided to explain key concepts in classification and how different algorithms work.
Data Science Salon: Introduction to Machine Learning - Marketing Use CaseFormulatedby
This document provides an introduction and agenda for a machine learning marketing use case presentation. It includes an overview of the data science process, machine learning algorithms, and examples of machine learning in marketing. It discusses data preparation, feature selection, preprocessing, transformation, and algorithm selection. It also provides a primer on deep learning, the benefits of deep learning for feature extraction, and examples of innovations using deep learning. The presentation aims to help understand how to apply machine learning and deep learning techniques to optimize marketing.
This document provides an introduction and agenda for a machine learning marketing use case presentation. It begins with introducing the presenter and their company Cup of Data, which is hiring data scientists. The basic agenda is then outlined, covering goals, the data science process, a machine learning primer, optimization techniques, and marketing examples. The remainder of the document dives deeper into each section of the agenda, providing overviews and explanations of topics like the data science workflow process, data preparation techniques, grouping algorithms, and deep learning.
This presentation attempts to explain some of the concepts used when describing data science, machine learning, and deep learning. IT also describes data science as a process, rather than as a set of specific tools and services.
This document provides an overview of a machine learning course. It outlines the course prerequisites, description, learning outcomes, structure, grading breakdown, and topics to be covered. The course aims to develop practical machine learning and data science skills by covering theoretical concepts and applying them to programming assignments. It will be conducted online and involve lectures, assessments, a group project, and final exam. Key machine learning topics to be covered include supervised learning, unsupervised learning, and applications.
This document provides an overview of an introduction to machine learning course, including:
- A description of the course content which covers Python programming, data visualization, supervised learning algorithms, regression, and unsupervised learning.
- An example of predicting bike share usage at different stations and the importance of understanding the problem and data.
- Guidance on exploring and visualizing data in Python to gain insights before applying machine learning algorithms.
Lessons learned from building practical deep learning systemsXavier Amatriain
1. There are many lessons to be learned from building practical deep learning systems, including choosing the right evaluation metrics, being thoughtful about your data and potential biases, and understanding dependencies between data, models, and systems.
2. It is important to optimize only what matters and beware of biases in your data. Simple models are often better than complex ones, and feature engineering is crucial.
3. Both supervised and unsupervised learning are important, and ensembles often perform best. Your AI infrastructure needs to support both experimentation and production.
Machine Learning for (DF)IR with Velociraptor: From Setting Expectations to a...Chris Hammerschmidt
achine Learning for DFIR with Velociraptor: From Setting Expectations to a Case Study
By Christian Hammerschmidt, PhD - Head of Engineering/ML, APTA Technologies
Machine learning (ML) or artificial intelligence (AI) often comes with great promise and large marketing budgets for cybersecurity, especially in monitoring (such as EDR/XDR solutions). Post-breach, it often turns out that the actual performance falls short of its promises.
In this talk, we’ll briefly look at ML for DFIR: What tasks can ML solve, generally speaking? What requirements do we have for a useful ML system in cybersecurity/DFIR contexts, such as reliability, robustness to attackers, and explainability? What makes ML difficult to apply in cybersecurity, e.g. when thinking about false alerts or attackers attempting to circumvent automated systems?
After discussing the basics, we look at ML for velociraptor:
How can we process forensic data collected with VQL using machine learning (with a typical Python/Jupyter/scikit-learn/PyTorch stack)?
And how can we build artifacts that run ML directly on each endpoint, avoiding central data collection?
The talk concludes with a case study, showing how we significantly reduced time to analyze EVTX files in incident response cases, saving thousands of USD in costs and reducing time to resolution.
Bio: Chris Hammerschmidt did his PhD research on machine learning methods for reverse engineering software systems. Now, he’s heading APTA Technologies, a start-up building machine learning tools to understand software behavior .
Affiliation: APTA Technologies, https://apta.tech
This document provides an overview of machine learning concepts covered in an Introduction to Machine Learning course. It discusses topics like binary and multiclass classification, evaluation metrics like precision and recall, imbalanced datasets, and algorithms like k-nearest neighbors, decision trees, support vector machines, and data projection techniques. Examples and illustrations are provided to explain key concepts in classification and how different algorithms work.
Data Science Salon: Introduction to Machine Learning - Marketing Use CaseFormulatedby
This document provides an introduction and agenda for a machine learning marketing use case presentation. It includes an overview of the data science process, machine learning algorithms, and examples of machine learning in marketing. It discusses data preparation, feature selection, preprocessing, transformation, and algorithm selection. It also provides a primer on deep learning, the benefits of deep learning for feature extraction, and examples of innovations using deep learning. The presentation aims to help understand how to apply machine learning and deep learning techniques to optimize marketing.
This document provides an introduction and agenda for a machine learning marketing use case presentation. It begins with introducing the presenter and their company Cup of Data, which is hiring data scientists. The basic agenda is then outlined, covering goals, the data science process, a machine learning primer, optimization techniques, and marketing examples. The remainder of the document dives deeper into each section of the agenda, providing overviews and explanations of topics like the data science workflow process, data preparation techniques, grouping algorithms, and deep learning.
This presentation attempts to explain some of the concepts used when describing data science, machine learning, and deep learning. IT also describes data science as a process, rather than as a set of specific tools and services.
This document provides an overview of a machine learning course. It outlines the course prerequisites, description, learning outcomes, structure, grading breakdown, and topics to be covered. The course aims to develop practical machine learning and data science skills by covering theoretical concepts and applying them to programming assignments. It will be conducted online and involve lectures, assessments, a group project, and final exam. Key machine learning topics to be covered include supervised learning, unsupervised learning, and applications.
This document provides a course catalog for the CYBRScore education program. It outlines over 30 individual courses across topics like development, forensics, ISACA certification preparation, malware analysis, networks, and pentesting. For each course, the document describes the target audience, objectives, and typical daily topics. Course lengths vary from 3 to 5 days. Hands-on labs and exercises are a core part of the learning approach. The goal is to provide practical skills and expertise needed for cybersecurity roles. Instructors are required to have current real-world experience.
Lessons Learned from Testing Machine Learning SoftwareChristian Ramirez
1) Machine learning software is difficult to test compared to traditional software because it is monolithic rather than modular, so changing one part affects the whole system.
2) When testing machine learning models, you need to understand what the models have been taught from the training data, including potential issues like spurious correlations, rather than just checking inputs and outputs.
3) Testing machine learning software effectively requires a good mathematical foundation as well as an understanding of different machine learning techniques and how to implement them.
Big data expo - machine learning in the elastic stack BigDataExpo
This document discusses machine learning capabilities in the Elastic Stack. It describes how machine learning algorithms can be used for tasks like time series anomaly detection, log message classification, and forecasting. Examples are provided of using unsupervised learning to detect changes in system behavior from time series data and unusual log messages. The Elastic Stack components involved in ingesting, enriching, visualizing, analyzing and alerting on machine learning results are also outlined.
K-12 Computing Education for the AI Era: From Data Literacy to Data AgencyHenriikka Vartiainen
1) The keynote presentation discusses the need to update K-12 computing education from a focus on computational thinking to include data literacy and data agency given the rise of artificial intelligence and machine learning.
2) Examples are provided of emerging approaches to teaching AI/ML concepts to students, such as workshops exploring image recognition tools and having students design their own machine learning applications.
3) Significant changes are needed in K-12 computing education due to differences between classical programming paradigms and modern data-driven AI, including new technical concepts, problem-solving approaches, sources of problems, and ethical considerations around topics like algorithmic bias and data privacy.
This document discusses using machine learning and big data technologies to improve security workflows. It describes the challenges of analyzing large amounts of security data from many sources to detect threats. Machine learning can help by analyzing patterns in the data at scale. The document introduces the Lambda Defense approach, which applies a lambda architecture to build a "central nervous system" for security. This combines batch and real-time machine learning models to detect threats based on both sequential and unordered behaviors.
Talk at 8th Annual Central and Eastern European Software Engineering Conference in Russia
Nov 2, 2012
Moscow, Russia
Note that original (downloadable) .pptx file has embedded videos.
This document provides an overview of an introduction to machine learning course, including:
1. The course covers machine learning theory, case studies, and technical tutorials over 25-30 contact hours as part of a total of 125 hours.
2. Students will learn to set up their coding environment using the Anaconda distribution to install useful machine learning packages and Jupyter Notebook.
3. An example real-world challenge of using machine learning to predict bike sharing demand at stations to determine when bikes need to be redistributed is presented.
The document discusses machine learning and how computers can be programmed to learn from examples without being explicitly programmed. It provides examples of machine learning applications like predicting house prices, text classification, and face detection. Additionally, it describes different types of learning problems including supervised learning, unsupervised learning, and classification problems.
The document discusses machine learning and how computers can be programmed to learn from examples without being explicitly programmed. It provides examples of machine learning applications like predicting house prices, text classification, and face detection. Additionally, it describes different types of learning problems including supervised learning, unsupervised learning, and classification problems.
230208 MLOps Getting from Good to Great.pptxArthur240715
1) MLOps is the process of maintaining machine learning models in production environments. It involves monitoring model performance over time and retraining models if needed due to data or concept drift.
2) The MLOps pipeline includes stages for data engineering, modelling, deployment, and monitoring. Key aspects are ensuring reproducibility, managing data processing pipelines, and defining deployment and monitoring strategies.
3) Successful MLOps requires automating model deployment, monitoring model and data metrics over time, and retraining models when performance degrades to keep models performing well as data evolves in production.
Recent Advances in Machine Learning: Bringing a New Level of Intelligence to ...Brocade
Presentation by Brocade Chief Scientist and Fellow, David Meyer, given at Orange Gardens July 2016. What is Machine Learning and what is all the excitement about?
An associated blog is available here: http://community.brocade.com/t5/CTO-Corner/Networking-Meets-Artificial-Intelligence-A-Glimpse-into-the-Very/ba-p/88196
Best Artificial Intelligence Course | Online program | certification course Learn and Build
Learn Understand and solve complex machine learning problems with programming language skills and become AI experts, explore opportunities for data engineering, AI engineering, Software engineering and a lot more. Get enrolled now, learn anywhere and get an online certification Artificial Intelligence course.
The document discusses object-oriented programming concepts including objects, classes, message passing, abstraction, encapsulation, inheritance, polymorphism, and dynamic binding. It provides examples and definitions for each concept. It also discusses how to represent real-world entities like a person or place as objects with states (attributes and values) and behaviors (methods). Classes are defined as blueprints that specify common properties and functionality for objects. The relationships between classes and objects are demonstrated.
Fundamentals of OOP (Object Oriented Programming)MD Sulaiman
The document discusses object-oriented programming concepts including objects, classes, message passing, abstraction, encapsulation, inheritance, polymorphism, and dynamic binding. It provides examples and definitions for each concept. It also covers basic class concepts like defining classes, creating objects, using constructors, and accessing instance variables and methods. The document appears to be teaching material for an introductory object-oriented programming course.
chalenges and apportunity of deep learning for big data analysis fmaru kindeneh
The document discusses challenges and opportunities in analyzing complex data using deep learning. It begins with an introduction to complex data and deep learning. It then provides background on machine learning, different data types, feature engineering, and challenges in deep learning. The problem specification defines complex data and proposes research questions on how deep learning can better handle complex data properties. The method section outlines a literature review and case studies to define complex data and study its impact on deep learning models.
This use case showcases how Machine Learning can help you understand your customers to better develop personalized relationships. The lecturer is Arturo Moreno, Associate Professor at ICADE Business School, and a technology entrepreneur, investor, and innovative leader working on the intersection of venture capital and Machine Learning.
*Machine Learning School for Business Schools 2021: Virtual Conference.
الموعد الإثنين 03 يناير 2022
143
مبادرة
#تواصل_تطوير
المحاضرة ال 143 من المبادرة
المهندس / محمد الرافعي طرباي
نقيب المبرمجين بالدقهلية
بعنوان
"IT INDUSTRY"
How To Getting Into IT With Zero Experience
وذلك يوم الإثنين 03 يناير2022
السابعة مساء توقيت القاهرة
الثامنة مساء توقيت مكة المكرمة
و الحضور من تطبيق زووم
https://us02web.zoom.us/meeting/register/tZUpf-GsrD4jH9N9AxO39J013c1D4bqJNTcu
علما ان هناك بث مباشر للمحاضرة على القنوات الخاصة بجمعية المهندسين المصريين
ونأمل أن نوفق في تقديم ما ينفع المهندس ومهمة الهندسة في عالمنا العربي
والله الموفق
للتواصل مع إدارة المبادرة عبر قناة التليجرام
https://t.me/EEAKSA
ومتابعة المبادرة والبث المباشر عبر نوافذنا المختلفة
رابط اللينكدان والمكتبة الالكترونية
https://www.linkedin.com/company/eeaksa-egyptian-engineers-association/
رابط قناة التويتر
https://twitter.com/eeaksa
رابط قناة الفيسبوك
https://www.facebook.com/EEAKSA
رابط قناة اليوتيوب
https://www.youtube.com/user/EEAchannal
رابط التسجيل العام للمحاضرات
https://forms.gle/vVmw7L187tiATRPw9
ملحوظة : توجد شهادات حضور مجانية لمن يسجل فى رابط التقيم اخر المحاضرة
Accelerating the Pace of Engineering Education with Simulation, Hardware and ...Joachim Schlosser
Presentation for MathWorks (www.mathworks.com) at World Engineering Education Forum 2014, Dubai.
Education is challenging. It always was challenging, and it always will be challenging, but every generation of educators and society has to find answers specific to their era. This talk addresses some of the challenges in engineering education in the 21st century.
Industry complains about the skills gap they face with graduates in engineering, for lack of project awareness, problem solving skills, applicable tool skills or applied science skills. Academia complains about students not bringing the necessary basic skills as engineering freshmen. Teachers complain about a lack of student engagement. Students complain about classes not engaging them and seeming irrelevant.
When putting this chain of challenges – industry, academia, school, students – on its head and starting with the student engagement, one method getting attention is Project-Based Learning. Students educate themselves on concepts they need, with the teacher facilitating the learning experience. Applying theory in practical ways with tools that are used in industry gives students first-hand experience on industry relevant methods as well as the why behind theory. The talk shows examples of programming, modeling and simulation to gain insight into theory and application.
Too often students and educators feel that topics throughout their education are not connected. Early on they lack understanding of the why they are learning something. Later they no longer see the connection of advanced theory to fundamental concepts. Reusing learning artifacts, skills and methods helps mapping out the story. Demonstrations illustrate how educators implement this re-use throughout teaching.
Consequent reuse leads to Integrated Curriculum, where methods and skills in each year build on previous ones. Evaluations in integrated curriculum enabled programs show a higher retention of know-how.
We all can make math, physics and engineering able to experience using simulation and hardware experiments. The tools and resources are there. Let's address our generation's engineering education challenges.
This document provides a course catalog for the CYBRScore education program. It outlines over 30 individual courses across topics like development, forensics, ISACA certification preparation, malware analysis, networks, and pentesting. For each course, the document describes the target audience, objectives, and typical daily topics. Course lengths vary from 3 to 5 days. Hands-on labs and exercises are a core part of the learning approach. The goal is to provide practical skills and expertise needed for cybersecurity roles. Instructors are required to have current real-world experience.
Lessons Learned from Testing Machine Learning SoftwareChristian Ramirez
1) Machine learning software is difficult to test compared to traditional software because it is monolithic rather than modular, so changing one part affects the whole system.
2) When testing machine learning models, you need to understand what the models have been taught from the training data, including potential issues like spurious correlations, rather than just checking inputs and outputs.
3) Testing machine learning software effectively requires a good mathematical foundation as well as an understanding of different machine learning techniques and how to implement them.
Big data expo - machine learning in the elastic stack BigDataExpo
This document discusses machine learning capabilities in the Elastic Stack. It describes how machine learning algorithms can be used for tasks like time series anomaly detection, log message classification, and forecasting. Examples are provided of using unsupervised learning to detect changes in system behavior from time series data and unusual log messages. The Elastic Stack components involved in ingesting, enriching, visualizing, analyzing and alerting on machine learning results are also outlined.
K-12 Computing Education for the AI Era: From Data Literacy to Data AgencyHenriikka Vartiainen
1) The keynote presentation discusses the need to update K-12 computing education from a focus on computational thinking to include data literacy and data agency given the rise of artificial intelligence and machine learning.
2) Examples are provided of emerging approaches to teaching AI/ML concepts to students, such as workshops exploring image recognition tools and having students design their own machine learning applications.
3) Significant changes are needed in K-12 computing education due to differences between classical programming paradigms and modern data-driven AI, including new technical concepts, problem-solving approaches, sources of problems, and ethical considerations around topics like algorithmic bias and data privacy.
This document discusses using machine learning and big data technologies to improve security workflows. It describes the challenges of analyzing large amounts of security data from many sources to detect threats. Machine learning can help by analyzing patterns in the data at scale. The document introduces the Lambda Defense approach, which applies a lambda architecture to build a "central nervous system" for security. This combines batch and real-time machine learning models to detect threats based on both sequential and unordered behaviors.
Talk at 8th Annual Central and Eastern European Software Engineering Conference in Russia
Nov 2, 2012
Moscow, Russia
Note that original (downloadable) .pptx file has embedded videos.
This document provides an overview of an introduction to machine learning course, including:
1. The course covers machine learning theory, case studies, and technical tutorials over 25-30 contact hours as part of a total of 125 hours.
2. Students will learn to set up their coding environment using the Anaconda distribution to install useful machine learning packages and Jupyter Notebook.
3. An example real-world challenge of using machine learning to predict bike sharing demand at stations to determine when bikes need to be redistributed is presented.
The document discusses machine learning and how computers can be programmed to learn from examples without being explicitly programmed. It provides examples of machine learning applications like predicting house prices, text classification, and face detection. Additionally, it describes different types of learning problems including supervised learning, unsupervised learning, and classification problems.
The document discusses machine learning and how computers can be programmed to learn from examples without being explicitly programmed. It provides examples of machine learning applications like predicting house prices, text classification, and face detection. Additionally, it describes different types of learning problems including supervised learning, unsupervised learning, and classification problems.
230208 MLOps Getting from Good to Great.pptxArthur240715
1) MLOps is the process of maintaining machine learning models in production environments. It involves monitoring model performance over time and retraining models if needed due to data or concept drift.
2) The MLOps pipeline includes stages for data engineering, modelling, deployment, and monitoring. Key aspects are ensuring reproducibility, managing data processing pipelines, and defining deployment and monitoring strategies.
3) Successful MLOps requires automating model deployment, monitoring model and data metrics over time, and retraining models when performance degrades to keep models performing well as data evolves in production.
Recent Advances in Machine Learning: Bringing a New Level of Intelligence to ...Brocade
Presentation by Brocade Chief Scientist and Fellow, David Meyer, given at Orange Gardens July 2016. What is Machine Learning and what is all the excitement about?
An associated blog is available here: http://community.brocade.com/t5/CTO-Corner/Networking-Meets-Artificial-Intelligence-A-Glimpse-into-the-Very/ba-p/88196
Best Artificial Intelligence Course | Online program | certification course Learn and Build
Learn Understand and solve complex machine learning problems with programming language skills and become AI experts, explore opportunities for data engineering, AI engineering, Software engineering and a lot more. Get enrolled now, learn anywhere and get an online certification Artificial Intelligence course.
The document discusses object-oriented programming concepts including objects, classes, message passing, abstraction, encapsulation, inheritance, polymorphism, and dynamic binding. It provides examples and definitions for each concept. It also discusses how to represent real-world entities like a person or place as objects with states (attributes and values) and behaviors (methods). Classes are defined as blueprints that specify common properties and functionality for objects. The relationships between classes and objects are demonstrated.
Fundamentals of OOP (Object Oriented Programming)MD Sulaiman
The document discusses object-oriented programming concepts including objects, classes, message passing, abstraction, encapsulation, inheritance, polymorphism, and dynamic binding. It provides examples and definitions for each concept. It also covers basic class concepts like defining classes, creating objects, using constructors, and accessing instance variables and methods. The document appears to be teaching material for an introductory object-oriented programming course.
chalenges and apportunity of deep learning for big data analysis fmaru kindeneh
The document discusses challenges and opportunities in analyzing complex data using deep learning. It begins with an introduction to complex data and deep learning. It then provides background on machine learning, different data types, feature engineering, and challenges in deep learning. The problem specification defines complex data and proposes research questions on how deep learning can better handle complex data properties. The method section outlines a literature review and case studies to define complex data and study its impact on deep learning models.
This use case showcases how Machine Learning can help you understand your customers to better develop personalized relationships. The lecturer is Arturo Moreno, Associate Professor at ICADE Business School, and a technology entrepreneur, investor, and innovative leader working on the intersection of venture capital and Machine Learning.
*Machine Learning School for Business Schools 2021: Virtual Conference.
الموعد الإثنين 03 يناير 2022
143
مبادرة
#تواصل_تطوير
المحاضرة ال 143 من المبادرة
المهندس / محمد الرافعي طرباي
نقيب المبرمجين بالدقهلية
بعنوان
"IT INDUSTRY"
How To Getting Into IT With Zero Experience
وذلك يوم الإثنين 03 يناير2022
السابعة مساء توقيت القاهرة
الثامنة مساء توقيت مكة المكرمة
و الحضور من تطبيق زووم
https://us02web.zoom.us/meeting/register/tZUpf-GsrD4jH9N9AxO39J013c1D4bqJNTcu
علما ان هناك بث مباشر للمحاضرة على القنوات الخاصة بجمعية المهندسين المصريين
ونأمل أن نوفق في تقديم ما ينفع المهندس ومهمة الهندسة في عالمنا العربي
والله الموفق
للتواصل مع إدارة المبادرة عبر قناة التليجرام
https://t.me/EEAKSA
ومتابعة المبادرة والبث المباشر عبر نوافذنا المختلفة
رابط اللينكدان والمكتبة الالكترونية
https://www.linkedin.com/company/eeaksa-egyptian-engineers-association/
رابط قناة التويتر
https://twitter.com/eeaksa
رابط قناة الفيسبوك
https://www.facebook.com/EEAKSA
رابط قناة اليوتيوب
https://www.youtube.com/user/EEAchannal
رابط التسجيل العام للمحاضرات
https://forms.gle/vVmw7L187tiATRPw9
ملحوظة : توجد شهادات حضور مجانية لمن يسجل فى رابط التقيم اخر المحاضرة
Accelerating the Pace of Engineering Education with Simulation, Hardware and ...Joachim Schlosser
Presentation for MathWorks (www.mathworks.com) at World Engineering Education Forum 2014, Dubai.
Education is challenging. It always was challenging, and it always will be challenging, but every generation of educators and society has to find answers specific to their era. This talk addresses some of the challenges in engineering education in the 21st century.
Industry complains about the skills gap they face with graduates in engineering, for lack of project awareness, problem solving skills, applicable tool skills or applied science skills. Academia complains about students not bringing the necessary basic skills as engineering freshmen. Teachers complain about a lack of student engagement. Students complain about classes not engaging them and seeming irrelevant.
When putting this chain of challenges – industry, academia, school, students – on its head and starting with the student engagement, one method getting attention is Project-Based Learning. Students educate themselves on concepts they need, with the teacher facilitating the learning experience. Applying theory in practical ways with tools that are used in industry gives students first-hand experience on industry relevant methods as well as the why behind theory. The talk shows examples of programming, modeling and simulation to gain insight into theory and application.
Too often students and educators feel that topics throughout their education are not connected. Early on they lack understanding of the why they are learning something. Later they no longer see the connection of advanced theory to fundamental concepts. Reusing learning artifacts, skills and methods helps mapping out the story. Demonstrations illustrate how educators implement this re-use throughout teaching.
Consequent reuse leads to Integrated Curriculum, where methods and skills in each year build on previous ones. Evaluations in integrated curriculum enabled programs show a higher retention of know-how.
We all can make math, physics and engineering able to experience using simulation and hardware experiments. The tools and resources are there. Let's address our generation's engineering education challenges.
How to Download & Install Module From the Odoo App Store in Odoo 17Celine George
Custom modules offer the flexibility to extend Odoo's capabilities, address unique requirements, and optimize workflows to align seamlessly with your organization's processes. By leveraging custom modules, businesses can unlock greater efficiency, productivity, and innovation, empowering them to stay competitive in today's dynamic market landscape. In this tutorial, we'll guide you step by step on how to easily download and install modules from the Odoo App Store.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
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إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
💀💀💀💀💀💀💀💀💀💀
تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
#فهم_ماكو_درخ
3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊
🔥🔥🔥🔥🔥🔥🔥🔥🔥
CapTechTalks Webinar Slides June 2024 Donovan Wright.pptxCapitolTechU
Slides from a Capitol Technology University webinar held June 20, 2024. The webinar featured Dr. Donovan Wright, presenting on the Department of Defense Digital Transformation.
2. Trinity College Dublin, The University of Dublin
Quote Slide Option 2 Lecture outline
• Module outline and schedule
• Module objectives and evaluation criteria
• What is Machine Learning (ML)
• What can we do with ML
3. Trinity College Dublin, The University of Dublin
Quote Slide Option 2 Course outline
• (Introduction to programming – Python)
• Data visualisation
• Supervised learning
• Classification
• Regression and time-series
• Overfitting and regularisation
• Unsupervised learning
• Clustering
• Dimensionality reduction
• Model tuning, evaluation, and Feature selection
• Tutorials + continuous assessment on lab exercises
• Examples on publicly available data
• Discussion about current and future challenges for ML
• E.g., data sharing, anonymisation and privacy, standardisation
4. Trinity College Dublin, The University of Dublin 4
Week LECTURE LAB Additional hour and deadlines
1 Overview Introduction to programming and Python
programming tutorial
2 Descriptive stats vs. ML. Data visual.
Discussion board task explanation
Python programming and basic
visualisation tutorial
3 Supervised Learn. Simple classifiers Data visualisation and simple classification
tutorial
4 Classification – part 1 Sup.L. Model quality evaluation tutorial Data preparation and visualization
test (1h) – immediately after the tutorial
5 Classification – part 2: algorithms
Homework 1 explanation
Classification tutorial
6 Classification part 3 and Regression Regression and time-series tutorial
7 Reading week -
8 Regression
Homework 2 explanation
Unsupervised learning tutorial Homework 1 deadline
9 Regression and Unsupervised learning Homework hour with Q&A
10 Recap and feature selection Anomaly detection tutorial
11 Data sharing, storage, and privacy Homework hour with Q&A Homework 2 deadline
12 Guest lecture Discussion
Introduction to Machine Learning – 2022-2023
Written test (2h)
5. Trinity College Dublin, The University of Dublin 5
Evaluation
Quote Slide Option 2
• Laboratory test (10%)
• Individual homework (25%)
• Group assignment (25%)
• Written test (40%)
Theory
Technical skills
Communication
5 ECTS = 125h
6. Trinity College Dublin, The University of Dublin 6
Useful references
Quote Slide Option 2
• Python tutorial
https://coherentpdf.com/python/pythonfromtheverybeginning.html
• Jupyter-notebook tutorial
https://www.dataquest.io/blog/jupyter-notebook-tutorial/
“Hands-On Machine Learning with Scikit-Learn,
Keras, and TensorFlow”, Aurélien Géron, 2019
7. Trinity College Dublin, The University of Dublin 7
Introduction to ML
Quote Slide Option 2
- ML (what is it?
How does it work?
How do we use it?)
- Most common algorithms
- Challenges in the context of
smart-cities
Theory
Technical skills
Communication
- Introduction to computer programming
- Python
- Data visualization
- Data analysis
- ML
- The concept/algorithm matters more
than the syntax or issues with libraries!
- Expert – non-expert interaction
- Problem -> specify requirements ->
understand challenges -> interpret
results
8. Trinity College Dublin, The University of Dublin 8
Introduction to ML
Quote Slide Option 2
Theory
Technical skills
Communication
- This module will give you the basics. A good start, but you won’t be ML experts (but you can get there!)
- You will learn how to communicate with ML experts
- It is essential that you play around with the code, try different variations on other datasets. Re-running our tutorials
won’t be enough
- Interact with us! Ask questions. Use the online resources. Don’t wait, start straight away!
9. Trinity College Dublin, The University of Dublin 9
Introduction to ML
Quote Slide Option 2
- Issues with the code during the assessment/labs? No problem. Ask us questions or use pseudocode to describe
exactly what you wanted to do
Use the Internet!
10. Trinity College Dublin, The University of Dublin 10
Introduction to ML
Quote Slide Option 2
- Let’s not reinvent the wheel!
- We will study what tools are available, how they work, and how to best use them!
- But not black-box style!
11. Trinity College Dublin, The University of Dublin 11
Your Continuous Feedback is Important!
Quote Slide Option 2
12. Trinity College Dublin, The University of Dublin 12
Your Background and Prior Experience
Quote Slide Option 2
• Laptop/computer
• Excel
• OS (Operating System)
• Coding (Python)
• What’s a variable
• Integer, double, logical
• for loop, function, class?
• What’s a compiler?
13. Trinity College Dublin, The University of Dublin 13
https://jupyter.readthedocs.io/en/latest/install/notebook-classic.html
Setting up your coding environment
Download and install Anaconda
- Windows or Mac OS: run Anaconda Navigator from
the Start menu or application menu
- In Linux: run anaconda-navigator from the terminal
14. Trinity College Dublin, The University of Dublin 14
What is Artificial Intelligence (AI)?
Quote Slide Option 2
15. Trinity College Dublin, The University of Dublin 15
What is Artificial Intelligence (AI)?
Quote Slide Option 2
https://www.youtube.com/watch?v=760lA2YCKjM
https://www.youtube.com/watch?v=78-1MlkxyqI
16. Trinity College Dublin, The University of Dublin 16
What is Artificial Intelligence (AI)?
Quote Slide Option 2
Weak AI
Strong AI
“Artificial intelligence: A modern approach”,
Russel and Norvig
• The study of intelligent agents
• Systems/devices that perceive their
environment and take actions in that
environment to achieve their goals
• Weak AI: “Intelligent” actions (like or better
than humans)
• Strong AI: “Intelligent” thinking
17. Trinity College Dublin, The University of Dublin 17
What is machine learning (ML)?
Quote Slide Option 2
AI
ML
“Hands-On Machine Learning with Scikit-Learn,
Keras, and TensorFlow”, Aurélien Géron, 2019
Learning can be seen as a process for improving performance
based on experience
To be defined
To be defined
18. Trinity College Dublin, The University of Dublin 18
What is machine learning (ML)?
Quote Slide Option 2
Instead of trying to produce a programme to simulate the adult mind, why not
rather try to produce one which simulates the child's? [Alan Turing, 1950]
19. Trinity College Dublin, The University of Dublin 19
What is machine learning (ML)?
Quote Slide Option 2
“Hands-On Machine Learning with Scikit-Learn,
Keras, and TensorFlow”, Aurélien Géron, 2019
20. Trinity College Dublin, The University of Dublin 20
What can we do with ML?
Quote Slide Option 2
“Hands-On Machine Learning with Scikit-Learn,
Keras, and TensorFlow”, Aurélien Géron, 2019
ML is generally about learning patterns in the data and use that information for our goals (e.g., spam filter)
How can we learn these patterns?
We don’t tell the algorithm how to detect spam emails.
We give the ML algorithm examples of spam emails. Then, it has to figure out how to detect them.
21. Trinity College Dublin, The University of Dublin 21
What can we do with ML?
Quote Slide Option 2
Supervised learning
• Classification
• Regression
Unsupervised learning
“Hands-On Machine Learning with Scikit-Learn,
Keras, and TensorFlow”, Aurélien Géron, 2019
22. Trinity College Dublin, The University of Dublin 22
Supervised Learning
Quote Slide Option 2
y = f(X)
f ynew
Model Training (learning or fit)
Xnew
f y
X
Using the model (test)
known known
unknown known
known unknown
23. Trinity College Dublin, The University of Dublin 23
Supervised Learning
Quote Slide Option 2
Classification example
X (Data)
Y (Labels – desired output)
1 1 1 1 2 2 2 2 3 3 3 3
New unseen image
ML_model: y = f(X) ML
model
2
Good
prediction
24. Trinity College Dublin, The University of Dublin 24
Unsupervised Learning
Quote Slide Option 2
Clustering example
X (Data)
ML
model
25. Trinity College Dublin, The University of Dublin 25
What can we do with ML?
Quote Slide Option 2
“Hands-On Machine Learning with Scikit-Learn,
Keras, and TensorFlow”, Aurélien Géron, 2019
In the simplest case, the ML model learns just once. It will not learn later on or change automatically.
So, it learns once and then it is used as an anomaly detection tool.
X = [feature1, feature2, …, featurek]
26. Trinity College Dublin, The University of Dublin 26
What can we do with ML?
Quote Slide Option 2
“Hands-On Machine Learning with Scikit-Learn,
Keras, and TensorFlow”, Aurélien Géron, 2019
Reinforcement learning
- Like humans, these algorithms
learn over time (one step at a
time)
- Learn from the outcome (e.g.,
“good” or “bad”) of your actions.
Learn from your successes and
mistakes
27. Trinity College Dublin, The University of Dublin 27
What can we do with ML?
Quote Slide Option 2
Real life data are messy! Noisy data, missing data, artifacts, mislabelled data.
https://platerecognizer.com/blueiris/
28. Trinity College Dublin, The University of Dublin 28
Objectives and learning outcomes
Quote Slide Option 2
• MLO1 Configure a programming environment suitable for exploring ML techniques
• MLO2 Prepare datasets for ML processing, visualise the data, and understand the consequences of decisions made in cleaning data
• MLO3 Assess the performance of a ML pipeline
• MLO4 Critically evaluate the outputs of a ML pipeline
• MLO5 Communicate with ML experts and non-experts: Explain goals and requirements of a project, interpret the outcomes of typical ML
analyses, present results to non-experts.
• MLO6 Assess the cost/benefit of distinct ML methodologies and explain what makes one approach more suitable than another one for a
given task
• MLO7. Understand challenges involving data sharing, storage, and privacy
Problem/question Data collection
Preprocessing /
cleaning
Analysing
Interpretation /
outcome
Improve
ML
29. Trinity College Dublin, The University of Dublin 29
What can we do with ML?
Quote Slide Option 2
“Hands-On Machine Learning with Scikit-Learn,
Keras, and TensorFlow”, Aurélien Géron, 2019
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