Part 3 (machine learning overview) the forms of knowledge
•
0 likes•592 views
In Part 3 of Fast Track Machine Learning (Machine Learning Overview) series Dr. Dakshinamurthy Kolluru explains that as a Machine Learning expert one has to give more importance to the 'Customer' rather than the way algorithm is developed. Based on customer's requirement, finalize the output forms of knowledge. The form could be a rule or equation or graph or a black box.
Report
Share
Report
Share
Download to read offline
Recommended
Part 5 (machine learning overview) what makes a problem tough
In this part 5 of Fast Track Machine Learning (Machine Learning Overview) series, Dr. Dakshinamurthy Kolluru explains about the different aspects which makes a data science problem tough.
He says that it is easy to work with structured data rather than an unstructured data and explains why it is so.
Fast Track Machine Learning Part 1 (Machine Learning Overview) - Types of Mac...
Fast Track Machine Learning Part 1 (Machine Learning Overview) - Types of Mac...International School of Engineering
In part 1 of Fast track Machine learning (Machine Learning Overview) series Dr. Dakshinamurthy Kolluru gives a thousand feet view of Machine Learning.
One can divide the problems in Machine Learning as Classification, Regression and Optimization. Where in
Classification can be defined as splitting the space.
Regression is fitting a curve and regression can also be set up as a classification problem.
Optimization is on a curve, find the maximum and minimum points.Part 4 (machine learning overview) solution architecture
In part 4 of Fast Track Machine Learning (Machine Learning Overview) series, Dr. Dakshinamurthy Kolluru explains about the three main aspects that are to be given importance while defining the architecture in Machine Learning.
He explains about the difference between training, testing data and why is it important to keep testing data in a given data set.
Part 2 (machine learning overview) all machine learning is pattern search
Part 2 (machine learning overview) all machine learning is pattern searchInternational School of Engineering
In Part 2 of Fast Track Machine Learning (Machine Learning Overview) series Dr. Dakshinamurthy Kolluru explains that all Machine Learning can be treated as Pattern Search.
The 5 different searches in Machine Learning are:
1. Exhaustive Search
2. Random Search
3. Mathematical Search
4. Greedy Search
5. Guided Random Search
He explains all the five with the help of different real-world examples.Part 6 (machine learning overview) what makes a problem tough continued
Part 6 (machine learning overview) what makes a problem tough continuedInternational School of Engineering
In this part 6 of Fast Track Machine Learning (Machine Learning Overview) series, Dr. Dakshinamurthy Kolluru explains about the Big Data.
He explains about Big Data and how the issue is resolved using Big Data. He also explains what is Pig, Hive, Hadoop.عرض 1
The document discusses two articles on the use of tablets in education. The first article notes that tablets can be effective instructional tools for teaching English as a foreign language by providing learning anywhere and anytime. However, it notes teachers need training to use tablets and more writing applications need to be developed. The second article discusses using tablets in mathematics education. It states tablets can make mathematics classes more enjoyable with visuals and help conceptual understanding, but may lessen teacher-student interaction and protecting the devices could be difficult. Both articles examine the benefits and challenges of using tablets in different subject areas.
E-commerce: Slides template
This presentation discusses a topic related to ICT (information and communications technology) in education. It includes sections on introduction, motivation, importance and scope, comparisons with other countries, findings, summary, and conclusion. The presentation provides guidelines for students, including writing detailed descriptions in each section and trying to include figures, pictures, videos, and references. It is to be shared on social media platforms by the deadline of October 15, 2013.
Resume shaojie yan
I got my bachelor's degree of mechanical engineering in XJTU, and am pursuing master of science degree in TAMU now. I have two interns as practice mechanical engineer and many projects about controller design and coding. I'm interested in autonomous vehicle control as well as control theory and actively seeking a full-time job starting at June, 2020.
Recommended
Part 5 (machine learning overview) what makes a problem tough
In this part 5 of Fast Track Machine Learning (Machine Learning Overview) series, Dr. Dakshinamurthy Kolluru explains about the different aspects which makes a data science problem tough.
He says that it is easy to work with structured data rather than an unstructured data and explains why it is so.
Fast Track Machine Learning Part 1 (Machine Learning Overview) - Types of Mac...
Fast Track Machine Learning Part 1 (Machine Learning Overview) - Types of Mac...International School of Engineering
In part 1 of Fast track Machine learning (Machine Learning Overview) series Dr. Dakshinamurthy Kolluru gives a thousand feet view of Machine Learning.
One can divide the problems in Machine Learning as Classification, Regression and Optimization. Where in
Classification can be defined as splitting the space.
Regression is fitting a curve and regression can also be set up as a classification problem.
Optimization is on a curve, find the maximum and minimum points.Part 4 (machine learning overview) solution architecture
In part 4 of Fast Track Machine Learning (Machine Learning Overview) series, Dr. Dakshinamurthy Kolluru explains about the three main aspects that are to be given importance while defining the architecture in Machine Learning.
He explains about the difference between training, testing data and why is it important to keep testing data in a given data set.
Part 2 (machine learning overview) all machine learning is pattern search
Part 2 (machine learning overview) all machine learning is pattern searchInternational School of Engineering
In Part 2 of Fast Track Machine Learning (Machine Learning Overview) series Dr. Dakshinamurthy Kolluru explains that all Machine Learning can be treated as Pattern Search.
The 5 different searches in Machine Learning are:
1. Exhaustive Search
2. Random Search
3. Mathematical Search
4. Greedy Search
5. Guided Random Search
He explains all the five with the help of different real-world examples.Part 6 (machine learning overview) what makes a problem tough continued
Part 6 (machine learning overview) what makes a problem tough continuedInternational School of Engineering
In this part 6 of Fast Track Machine Learning (Machine Learning Overview) series, Dr. Dakshinamurthy Kolluru explains about the Big Data.
He explains about Big Data and how the issue is resolved using Big Data. He also explains what is Pig, Hive, Hadoop.عرض 1
The document discusses two articles on the use of tablets in education. The first article notes that tablets can be effective instructional tools for teaching English as a foreign language by providing learning anywhere and anytime. However, it notes teachers need training to use tablets and more writing applications need to be developed. The second article discusses using tablets in mathematics education. It states tablets can make mathematics classes more enjoyable with visuals and help conceptual understanding, but may lessen teacher-student interaction and protecting the devices could be difficult. Both articles examine the benefits and challenges of using tablets in different subject areas.
E-commerce: Slides template
This presentation discusses a topic related to ICT (information and communications technology) in education. It includes sections on introduction, motivation, importance and scope, comparisons with other countries, findings, summary, and conclusion. The presentation provides guidelines for students, including writing detailed descriptions in each section and trying to include figures, pictures, videos, and references. It is to be shared on social media platforms by the deadline of October 15, 2013.
Resume shaojie yan
I got my bachelor's degree of mechanical engineering in XJTU, and am pursuing master of science degree in TAMU now. I have two interns as practice mechanical engineer and many projects about controller design and coding. I'm interested in autonomous vehicle control as well as control theory and actively seeking a full-time job starting at June, 2020.
Part 3 (machine learning overview) the forms of knowledge
In Part 3 of Fast Track Machine Learning (Machine Learning Overview) series Dr. Dakshinamurthy Kolluru explains that as a Machine Learning expert one has to give more importance to the 'Customer' rather than the way algorithm is developed.
Based on customer's requirement, finalize the output forms of knowledge. The form could be a rule or equation or graph or a black box.
Survey Performance Improvement Construct FP-Growth Tree
Construction of a compact FP-tree ensures that subsequent mining can be performed with a rather compact data structure. This does not automatically guarantee that it will be highly efficient since one may still encounter the combinatorial problem of candidate generation if one simply uses this FP-tree to generate and check all the candidate patterns. we study how to explore the compact information stored in an FP-tree, develop the principles of frequent-pattern growth by examination of our running example, explore how to perform further optimization when there exit a single prefix path in an FP-tree, and propose a frequent- pattern growth algorithm, FP-growth, for mining the complete set of frequent patterns using FP-tree.
Part 2 (machine learning overview) all machine learning is pattern search
Part 2 (machine learning overview) all machine learning is pattern searchInternational School of Engineering
In Part 2 of Fast Track Machine Learning (Machine Learning Overview) series Dr. Dakshinamurthy Kolluru explains that all Machine Learning can be treated as Pattern Search.
The 5 different searches in Machine Learning are:
1. Exhaustive Search
2. Random Search
3. Mathematical Search
4. Greedy Search
5. Guided Random Search
He explains all the five with the help of different real-world examples.Service assurance with predictive analytics
The document discusses the business challenge of downtime for Fortune 500 companies which costs an average of $46 million per year, and proposes a solution of using predictive analytics and real-time monitoring of IT services to proactively detect issues and reduce downtime costs by up to 80%. The solution architecture involves collecting data from various sources, using machine learning techniques like neural networks for analysis, and providing dashboards and reports to help customers reduce losses from unplanned outages.
Health Care Analytics
The document discusses strategies and solutions for accountable care organizations (ACOs) to improve patient outcomes and lower costs. It proposes an ACO Optimization Toolkit and ACO-ASK decision support system that use techniques like predictive modeling, text mining, and machine learning. Revenue projections show consulting revenues growing from $53 million in 2012 to over $200 million by 2018 as more ACOs adopt the solutions. The goal is to help providers succeed under new reimbursement models that emphasize quality and value over volume.
Part 4 (machine learning overview) solution architecture
The document discusses machine learning solution architecture. It explains that the goal is to establish relationships between data like customer demographics and transactions to predict outcomes like loan approvals. For a bank example, the problem is a binary classification of loan approval decisions. The architecture involves splitting data into training, testing, and evaluation sets to build and refine models without overfitting. Metrics to evaluate models include precision, sensitivity and specificity.
Engineering Big Data with Hadoop
The document discusses how digital data volumes are growing exponentially, with sources citing terabytes of daily tweets and petabytes of monthly search data. It provides an overview of the Hadoop ecosystem and architecture, explaining how Hadoop distributes and stores large amounts of data across clusters of commodity hardware to allow processing of data in parallel. Examples of Hadoop components are listed, along with how it aims to minimize data transfer and allow programmers to focus on application development rather than infrastructure details.
Scope and Career in Analytics
This document discusses the scope, growth, and career opportunities in analytics. It defines analytics as the process of analyzing large datasets to discover useful patterns and insights. Analytics helps organizations make better, faster decisions by identifying opportunities for improvement. The analytics market in India is worth $375 million currently and is expected to increase to $1.15 billion. Analytics jobs in India range from 500 to 800 analysts out of every 10,000 employees at a company. Salaries for analytics professionals increase with more years of experience, ranging from 3.2 lakhs for 0-2 years of experience to over 27 lakhs for more than 12 years of experience. The field of analytics is highly in demand and is considered the sexiest job of
Fp growth
An efficient and scalable method to complete set of frequent patterns.
It allow frequent item set discovery without candidate item set generation.
The comparative study of apriori and FP-growth algorithm
This document summarizes a seminar presentation comparing the Apriori and FP-Growth algorithms for association rule mining. The document introduces association rule mining and frequent itemset mining. It then describes the Apriori algorithm, including its generate-and-test approach and bottlenecks. Next, it explains the FP-Growth algorithm, including how it builds an FP-tree to efficiently extract frequent itemsets without candidate generation. Finally, it provides results comparing the performance of the two algorithms and concludes that FP-Growth is more efficient for mining long patterns.
Analytics in the Manufacturing industry
This presentation proposes an analytics product to predict failures in computer numerical controlled (CNC) machines used in manufacturing. It notes that CNC machines are prone to failures costing the industry billions annually. The proposed product would process machine logs, build prediction models using machine learning, and notify users when failures are predicted. It would provide predictions through a web dashboard and notifications. The presentation outlines the solution, costs, marketing approach, pricing models, and provides an example use case showing how the product could help optimize a ship manufacturing company's production line to increase profits.
Analytics in Supply Chain Management
BigSCM is a proposed product that uses big data from retail supply chains to optimize supply chain management processes. It collects data from RFID, POS, geo-location, social media, call centers, and more to provide recommendations for adaptive inventory management, demand prediction, price optimization, and more. This helps enhance productivity, optimize workflows, reduce costs and improve customer satisfaction. Developing BigSCM would require building out data processing and natural language processing capabilities over 6-8 months. It could help retailers optimize inventory costs, transportation costs, and procurement costs.
Fraud detection
This document summarizes a project to reduce fraudulent card transactions for a US national bank. An ensemble technique using logistic regression and K-nearest neighbors was developed to classify transactions as fraudulent or legitimate in real time. The project was estimated to reduce fraudulent losses by $16-18 million while costing $4.2 million to develop. Testing on 1 year of transaction data accurately classified transactions and reduced fraudulent cases by 80-90%, saving the bank $16 million.
Decision Trees
This document provides an overview of decision trees, including definitions, key terms, algorithms, and advantages/limitations. It defines a decision tree as a model that classifies instances by sorting them from the root to a leaf node. Important terms are defined like root node, branches, and leaf nodes. Popular algorithms like CART and C5.0 are described. Advantages are that decision trees are fast, robust, and require little experimentation. Limitations include class imbalance and overfitting with too many records and few attributes.
Apriori Algorithm
The document discusses the Apriori algorithm, which is used for mining frequent itemsets from transactional databases. It begins with an overview and definition of the Apriori algorithm and its key concepts like frequent itemsets, the Apriori property, and join operations. It then outlines the steps of the Apriori algorithm, provides an example using a market basket database, and includes pseudocode. The document also discusses limitations of the algorithm and methods to improve its efficiency, as well as advantages and disadvantages.
Analytics in Pharmaceutical Industry
This document discusses challenges and opportunities in the pharmaceutical industry. It notes that R&D costs are high due to extensive outsourcing, and benchmarking outsourced R&D units could lower costs by 40%. It also describes a software tool called PAT that would help predict side effects and check patent uniqueness of new drug formulas, saving 40% of time wasted on failed experiments. Overall the document analyzes ways to improve innovation and lower costs in pharmaceutical R&D through analytical solutions.
Watch and Watch and Watch again...TELFest
Presentation on using Echo360 lecture capture in Learning and Teaching. Presented at TELfest September 2014
Assignment 1 history of educational technology
This document provides instructions for an assignment on the history of education technology. Students are asked to:
1. Research key events in the history of education technology using recommended websites and other sources.
2. Create a 10-slide PowerPoint presentation summarizing the most relevant information and including citations.
3. Design a 10-point assessment form using Microsoft Word to evaluate classmates' presentations.
An extra activity encourages students to create an online timeline of important developments and share it with their class group.
Teaching All Students, Reaching All Learners
Universal Design for Learning (UDL) provides a framework for designing instruction that is flexible and accommodates learner differences. UDL principles include providing multiple means of representation, expression and engagement. The presentation provided examples of applying UDL in the classroom through diversifying instructional methods, materials and technologies. It also offered tips for making content more accessible such as using captions, audio descriptions and providing information in multiple formats.
Uses of ICT in MFL
This document provides suggestions for using information and communication technology (ICT) in the modern foreign language (MFL) classroom. It lists several websites where teachers can find ready-made MFL teaching resources and get inspiration from blogs. Some highlighted ICT tools for the MFL classroom include social media like Twitter, collaborative platforms like Padlet, flashcard creators like Quizlet, games like Quizdini and Sporcle, and QR codes to link students to online content.
I love free_nsta2010
The document provides an overview of the NSTA Area Conference 2010 held in Nashville, TN. It includes summaries and links for various educational technology tools and resources that were presented at the conference, including free PDF converters, portable antivirus software, photo album generators, digital storytelling software, online image editors, science simulations, educational videos and clips, and more. The document serves to inform educators about useful free and online educational resources that were highlighted at the conference.
Mat100 presentation
This document discusses integrating technology into a middle school probability curriculum. It recommends that students use online tools, games, videos and interactive websites to learn about probability concepts in an engaging way. Specific websites are recommended that provide tutorials, lessons, activities and practice problems related to probability. The document also discusses using online rubrics and wikis to assess students' understanding of probability.
More Related Content
Viewers also liked
Part 3 (machine learning overview) the forms of knowledge
In Part 3 of Fast Track Machine Learning (Machine Learning Overview) series Dr. Dakshinamurthy Kolluru explains that as a Machine Learning expert one has to give more importance to the 'Customer' rather than the way algorithm is developed.
Based on customer's requirement, finalize the output forms of knowledge. The form could be a rule or equation or graph or a black box.
Survey Performance Improvement Construct FP-Growth Tree
Construction of a compact FP-tree ensures that subsequent mining can be performed with a rather compact data structure. This does not automatically guarantee that it will be highly efficient since one may still encounter the combinatorial problem of candidate generation if one simply uses this FP-tree to generate and check all the candidate patterns. we study how to explore the compact information stored in an FP-tree, develop the principles of frequent-pattern growth by examination of our running example, explore how to perform further optimization when there exit a single prefix path in an FP-tree, and propose a frequent- pattern growth algorithm, FP-growth, for mining the complete set of frequent patterns using FP-tree.
Part 2 (machine learning overview) all machine learning is pattern search
Part 2 (machine learning overview) all machine learning is pattern searchInternational School of Engineering
In Part 2 of Fast Track Machine Learning (Machine Learning Overview) series Dr. Dakshinamurthy Kolluru explains that all Machine Learning can be treated as Pattern Search.
The 5 different searches in Machine Learning are:
1. Exhaustive Search
2. Random Search
3. Mathematical Search
4. Greedy Search
5. Guided Random Search
He explains all the five with the help of different real-world examples.Service assurance with predictive analytics
The document discusses the business challenge of downtime for Fortune 500 companies which costs an average of $46 million per year, and proposes a solution of using predictive analytics and real-time monitoring of IT services to proactively detect issues and reduce downtime costs by up to 80%. The solution architecture involves collecting data from various sources, using machine learning techniques like neural networks for analysis, and providing dashboards and reports to help customers reduce losses from unplanned outages.
Health Care Analytics
The document discusses strategies and solutions for accountable care organizations (ACOs) to improve patient outcomes and lower costs. It proposes an ACO Optimization Toolkit and ACO-ASK decision support system that use techniques like predictive modeling, text mining, and machine learning. Revenue projections show consulting revenues growing from $53 million in 2012 to over $200 million by 2018 as more ACOs adopt the solutions. The goal is to help providers succeed under new reimbursement models that emphasize quality and value over volume.
Part 4 (machine learning overview) solution architecture
The document discusses machine learning solution architecture. It explains that the goal is to establish relationships between data like customer demographics and transactions to predict outcomes like loan approvals. For a bank example, the problem is a binary classification of loan approval decisions. The architecture involves splitting data into training, testing, and evaluation sets to build and refine models without overfitting. Metrics to evaluate models include precision, sensitivity and specificity.
Engineering Big Data with Hadoop
The document discusses how digital data volumes are growing exponentially, with sources citing terabytes of daily tweets and petabytes of monthly search data. It provides an overview of the Hadoop ecosystem and architecture, explaining how Hadoop distributes and stores large amounts of data across clusters of commodity hardware to allow processing of data in parallel. Examples of Hadoop components are listed, along with how it aims to minimize data transfer and allow programmers to focus on application development rather than infrastructure details.
Scope and Career in Analytics
This document discusses the scope, growth, and career opportunities in analytics. It defines analytics as the process of analyzing large datasets to discover useful patterns and insights. Analytics helps organizations make better, faster decisions by identifying opportunities for improvement. The analytics market in India is worth $375 million currently and is expected to increase to $1.15 billion. Analytics jobs in India range from 500 to 800 analysts out of every 10,000 employees at a company. Salaries for analytics professionals increase with more years of experience, ranging from 3.2 lakhs for 0-2 years of experience to over 27 lakhs for more than 12 years of experience. The field of analytics is highly in demand and is considered the sexiest job of
Fp growth
An efficient and scalable method to complete set of frequent patterns.
It allow frequent item set discovery without candidate item set generation.
The comparative study of apriori and FP-growth algorithm
This document summarizes a seminar presentation comparing the Apriori and FP-Growth algorithms for association rule mining. The document introduces association rule mining and frequent itemset mining. It then describes the Apriori algorithm, including its generate-and-test approach and bottlenecks. Next, it explains the FP-Growth algorithm, including how it builds an FP-tree to efficiently extract frequent itemsets without candidate generation. Finally, it provides results comparing the performance of the two algorithms and concludes that FP-Growth is more efficient for mining long patterns.
Analytics in the Manufacturing industry
This presentation proposes an analytics product to predict failures in computer numerical controlled (CNC) machines used in manufacturing. It notes that CNC machines are prone to failures costing the industry billions annually. The proposed product would process machine logs, build prediction models using machine learning, and notify users when failures are predicted. It would provide predictions through a web dashboard and notifications. The presentation outlines the solution, costs, marketing approach, pricing models, and provides an example use case showing how the product could help optimize a ship manufacturing company's production line to increase profits.
Analytics in Supply Chain Management
BigSCM is a proposed product that uses big data from retail supply chains to optimize supply chain management processes. It collects data from RFID, POS, geo-location, social media, call centers, and more to provide recommendations for adaptive inventory management, demand prediction, price optimization, and more. This helps enhance productivity, optimize workflows, reduce costs and improve customer satisfaction. Developing BigSCM would require building out data processing and natural language processing capabilities over 6-8 months. It could help retailers optimize inventory costs, transportation costs, and procurement costs.
Fraud detection
This document summarizes a project to reduce fraudulent card transactions for a US national bank. An ensemble technique using logistic regression and K-nearest neighbors was developed to classify transactions as fraudulent or legitimate in real time. The project was estimated to reduce fraudulent losses by $16-18 million while costing $4.2 million to develop. Testing on 1 year of transaction data accurately classified transactions and reduced fraudulent cases by 80-90%, saving the bank $16 million.
Decision Trees
This document provides an overview of decision trees, including definitions, key terms, algorithms, and advantages/limitations. It defines a decision tree as a model that classifies instances by sorting them from the root to a leaf node. Important terms are defined like root node, branches, and leaf nodes. Popular algorithms like CART and C5.0 are described. Advantages are that decision trees are fast, robust, and require little experimentation. Limitations include class imbalance and overfitting with too many records and few attributes.
Apriori Algorithm
The document discusses the Apriori algorithm, which is used for mining frequent itemsets from transactional databases. It begins with an overview and definition of the Apriori algorithm and its key concepts like frequent itemsets, the Apriori property, and join operations. It then outlines the steps of the Apriori algorithm, provides an example using a market basket database, and includes pseudocode. The document also discusses limitations of the algorithm and methods to improve its efficiency, as well as advantages and disadvantages.
Analytics in Pharmaceutical Industry
This document discusses challenges and opportunities in the pharmaceutical industry. It notes that R&D costs are high due to extensive outsourcing, and benchmarking outsourced R&D units could lower costs by 40%. It also describes a software tool called PAT that would help predict side effects and check patent uniqueness of new drug formulas, saving 40% of time wasted on failed experiments. Overall the document analyzes ways to improve innovation and lower costs in pharmaceutical R&D through analytical solutions.
Viewers also liked (16)
Part 3 (machine learning overview) the forms of knowledge
Part 3 (machine learning overview) the forms of knowledge
Survey Performance Improvement Construct FP-Growth Tree
Survey Performance Improvement Construct FP-Growth Tree
Part 2 (machine learning overview) all machine learning is pattern search
Part 2 (machine learning overview) all machine learning is pattern search
Part 4 (machine learning overview) solution architecture
Part 4 (machine learning overview) solution architecture
The comparative study of apriori and FP-growth algorithm
The comparative study of apriori and FP-growth algorithm
Similar to Part 3 (machine learning overview) the forms of knowledge
Watch and Watch and Watch again...TELFest
Presentation on using Echo360 lecture capture in Learning and Teaching. Presented at TELfest September 2014
Assignment 1 history of educational technology
This document provides instructions for an assignment on the history of education technology. Students are asked to:
1. Research key events in the history of education technology using recommended websites and other sources.
2. Create a 10-slide PowerPoint presentation summarizing the most relevant information and including citations.
3. Design a 10-point assessment form using Microsoft Word to evaluate classmates' presentations.
An extra activity encourages students to create an online timeline of important developments and share it with their class group.
Teaching All Students, Reaching All Learners
Universal Design for Learning (UDL) provides a framework for designing instruction that is flexible and accommodates learner differences. UDL principles include providing multiple means of representation, expression and engagement. The presentation provided examples of applying UDL in the classroom through diversifying instructional methods, materials and technologies. It also offered tips for making content more accessible such as using captions, audio descriptions and providing information in multiple formats.
Uses of ICT in MFL
This document provides suggestions for using information and communication technology (ICT) in the modern foreign language (MFL) classroom. It lists several websites where teachers can find ready-made MFL teaching resources and get inspiration from blogs. Some highlighted ICT tools for the MFL classroom include social media like Twitter, collaborative platforms like Padlet, flashcard creators like Quizlet, games like Quizdini and Sporcle, and QR codes to link students to online content.
I love free_nsta2010
The document provides an overview of the NSTA Area Conference 2010 held in Nashville, TN. It includes summaries and links for various educational technology tools and resources that were presented at the conference, including free PDF converters, portable antivirus software, photo album generators, digital storytelling software, online image editors, science simulations, educational videos and clips, and more. The document serves to inform educators about useful free and online educational resources that were highlighted at the conference.
Mat100 presentation
This document discusses integrating technology into a middle school probability curriculum. It recommends that students use online tools, games, videos and interactive websites to learn about probability concepts in an engaging way. Specific websites are recommended that provide tutorials, lessons, activities and practice problems related to probability. The document also discusses using online rubrics and wikis to assess students' understanding of probability.
STEM Innovation in Education
This document discusses STEM innovation in education across multiple school districts in the St. Louis area. It includes presentations from Lindenwood University, St. Dominic High School, Orchard Farms School District, Francis Howell School District, Wentzville School District, Fort Zumwalt School District, Inventor Forge Makerspace, and Dave Engineering discussing their STEM programs, including the use of design software, robotics projects, Chromebook initiatives, music production courses, augmented reality applications, coding curriculum, makerspaces, and an initiative to donate old electronics to education programs.
Experiences with Inquiry-Based Learning in an Introductory Mechanics Course
The document summarizes a study on using inquiry-based learning in an introductory mechanics course. Students were assigned to take photos of objects around campus that demonstrated static equilibrium concepts and explain the forces and analysis required. 77% of submissions analyzed planar objects, while 23% analyzed more complex 3D examples. 3D analyses scored higher on accuracy. The study found students enjoyed the real-world assignment and it improved conceptual understanding over traditional problems. Based on the success, the instructor now uses inquiry-based assignments weekly and students are increasingly engaging with course concepts outside of class.
Instructional Strategies for Business Education Courses
This is a presentation that I gave at Bloomsburg University Instructional Strategies students and student teachers in March 2008.
Assignment 1 history of educational technology
This document provides the objectives and tasks for an assignment on the history of education technology. Students are asked to create a 10 slide PowerPoint presentation selecting relevant information from recommended websites on the topic and design a 10 point assessment form using Word. The tasks are to be completed individually or in pairs and there is an optional extra activity to create an online timeline of key events.
Assignment 1 history of educational technology
This document provides the objectives and tasks for an assignment on the history of education technology. Students are asked to create a 10 slide PowerPoint presentation selecting relevant information from recommended websites on the evolution of educational technology. They are also to create a 10 point assessment form in Microsoft Word and have the option to make an additional online timeline using provided web tools.
Uses of ICT in MFL
This document provides a list of ways that information and communication technologies (ICT) can be used in the modern foreign language (MFL) classroom. It lists websites where teachers can find ready-made resources, gives examples of blogs by MFL teachers to provide inspiration, and describes various digital tools like games, social media, and apps that can make foreign language learning more fun, motivating and relevant for students. The document emphasizes how ICT can engage students and help monitor their learning progress.
Powerpoint presentation on computer simulation,blended learning and education...
The document discusses computer simulation, blended learning, and educational podcasts. It provides definitions and examples of each. Computer simulation uses models and equations to represent real systems on a computer. Blended learning combines online and in-person instruction. There are several models of blended learning including face-to-face, rotation, and online driver. Educational podcasts allow audio and video to be shared for teaching and provide benefits like accessibility and creating an archive of lessons.
computer simulation, blended learning,educational podcasts
The document discusses computer simulation, blended learning, and educational podcasts. It provides definitions and examples of each. Computer simulation uses models and equations to represent real systems on a computer. Blended learning combines online and in-person learning, with various models including face-to-face instruction augmented by online tools. Educational podcasts allow audio and video to be shared for teaching, and have advantages like accessibility and creating an archive of lessons.
computer simulation,blended learning, educational podcasts
The document discusses computer simulation, blended learning, and educational podcasts. It provides definitions and examples of each. Computer simulation uses models and equations to represent real systems on a computer. Blended learning combines online and in-person learning, with various models including face-to-face instruction augmented by online tools. Educational podcasts allow audio and video to be shared for teaching, and have advantages like accessibility and creating an archive of lessons.
Open SUNY NDLW: Using open source virtual-reality environments for community...
Immersive, 3-D environments have offered opportunities for distance participants to share in any number of activities. With the advent of open source environments that are low-cost and that come either pre-configured or easily configured, this instructor has used these environments in class activities including: presentations, discussions, poster sessions, team meetings within a class, and shared activities (such as visiting other islands or testing 3-D building). Using action research the effectiveness of these environments on community building has been studied and published; students have overcome the isolation of discussion-board-driven online environments and been able to form more effective academic and personal relationships within courses.
EduMultiKG attains 92% accuracy in K-12 user profiling!
Disclaimer: This is a fictional paper written in a fashion that describes the present as 2043. In
this paper, we introduce a Semantic Web-based educational application for K-12 Automatic
User Modelling, which attains a 92% accuracy rate with a focus on enhancing personalised
learning experiences. The field of automatic user profiling has received considerable attention
in recent years, intending to enable personalised content delivery and individualised user
experience. In this paper, we present a novel machine learning-based approach for automatic user profiling in the K-12 education domain, which achieves a high accuracy compared to the baselines. Our approach involves the use of a combination of 10 out of 12 learning dimensions over the EduMultiKG tested in 152 students over the course of the academic year 2041-2042.
University Learning Spaces - Disciplinary Perspectives - Activities
Slides containing a selection of activities related to learning spaces in HE. A selection of these activities could be used in a workshop with the presentation, University Learning Spaces - Disciplinary Perspectives – Introduction.
Physics Educators as Designers of Simulation using Easy Java Simulation ( Ejs )
To deepen the professional practice of physics educators, we seek to highlight the Open Source Physics (OSP) and Easy Java Simulation (Ejs) community of educators that engage, enable and empower teachers as learners so that we can be leaders in our teaching practice. We learnt through Web 2 online collaborative means to develop simulations together with reputable physicists through the open source digital library. By examining the open source codes of the simulation through the Ejs toolkit, we are able to examine and make sense of the physics from the computational models created by practicing physicists. We will share some of the simulations that we have remixed from existing library of simulations models into suitable learning environments for inquiry of physics. http://www.phy.ntnu.edu.tw/ntnujava/index.php?board=28.0
Cept case study
The document provides an architectural case study of CEPT University in Ahmedabad, India. It describes the following key details:
- CEPT was founded in 1962 and was influenced by the designs of Louis Kahn and Le Corbusier, adopting an open plan concept.
- The 12.5 acre site is located in an educational zone and has a general slope towards the northwestern corner.
- Buildings are placed orthogonally along the cardinal directions around a central courtyard. Structures include studio blocks, administrative buildings, and workshops.
- Emphasis is placed on open, flexible spaces with exposure to natural light and connection to outdoor areas. Local materials keep costs low and allow for expansion.
Similar to Part 3 (machine learning overview) the forms of knowledge (20)
Experiences with Inquiry-Based Learning in an Introductory Mechanics Course
Experiences with Inquiry-Based Learning in an Introductory Mechanics Course
Instructional Strategies for Business Education Courses
Instructional Strategies for Business Education Courses
Powerpoint presentation on computer simulation,blended learning and education...
Powerpoint presentation on computer simulation,blended learning and education...
computer simulation, blended learning,educational podcasts
computer simulation, blended learning,educational podcasts
computer simulation,blended learning, educational podcasts
computer simulation,blended learning, educational podcasts
Open SUNY NDLW: Using open source virtual-reality environments for community...
Open SUNY NDLW: Using open source virtual-reality environments for community...
EduMultiKG attains 92% accuracy in K-12 user profiling!
EduMultiKG attains 92% accuracy in K-12 user profiling!
University Learning Spaces - Disciplinary Perspectives - Activities
University Learning Spaces - Disciplinary Perspectives - Activities
Physics Educators as Designers of Simulation using Easy Java Simulation ( Ejs )
Physics Educators as Designers of Simulation using Easy Java Simulation ( Ejs )
Recently uploaded
South African Journal of Science: Writing with integrity workshop (2024)
South African Journal of Science: Writing with integrity workshop (2024)Academy of Science of South Africa
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.ANATOMY AND BIOMECHANICS OF HIP JOINT.pdf
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
The History of Stoke Newington Street Names
Presented at the Stoke Newington Literary Festival on 9th June 2024
www.StokeNewingtonHistory.com
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar KanvariaPengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
How to Add Chatter in the odoo 17 ERP Module
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
Executive Directors Chat Leveraging AI for Diversity, Equity, and Inclusion
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
How to Manage Your Lost Opportunities in Odoo 17 CRM
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
Digital Artifact 1 - 10VCD Environments Unit
Digital Artifact 1 - 10VCD Environments Unit - NGV Pavilion Concept Design
Advanced Java[Extra Concepts, Not Difficult].docx
This is part 2 of my Java Learning Journey. This contains Hashing, ArrayList, LinkedList, Date and Time Classes, Calendar Class and more.
How to Make a Field Mandatory in Odoo 17
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
The Diamonds of 2023-2024 in the IGRA collection
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Recently uploaded (20)
South African Journal of Science: Writing with integrity workshop (2024)
South African Journal of Science: Writing with integrity workshop (2024)
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Executive Directors Chat Leveraging AI for Diversity, Equity, and Inclusion
Executive Directors Chat Leveraging AI for Diversity, Equity, and Inclusion
How to Manage Your Lost Opportunities in Odoo 17 CRM
How to Manage Your Lost Opportunities in Odoo 17 CRM
Part 3 (machine learning overview) the forms of knowledge
- 1. Inspire…Educate…Transform. Part 3 (Machine Learning Overview) - The Forms of Knowledge Dr. K. V Dakshinamurthy President, INSOFE The best place for students to learn Applied Engineering http://www.insofe.edu.in
- 2. The best place for students to learn Applied Engineering 2 http://www.insofe.edu.in
- 3. THE OUTPUT The best place for students to learn Applied Engineering 3 http://www.insofe.edu.in
- 4. Multiple forms • Rules –If x1, x2, x3 then Y • Equations –Y=f(x) The best place for students to learn Applied Engineering 4 http://www.insofe.edu.in
- 5. Graphs The best place for students to learn Applied Engineering 5 http://www.insofe.edu.in
- 6. Output • Similarities • Blackbox The best place for students to learn Applied Engineering 6 http://www.insofe.edu.in
- 7. YouTube link • For a detailed video on this topic visit the following link: http://www.youtube.com/watch?v=RcE nie0deiQ The best place for students to learn Applied Engineering 7 http://www.insofe.edu.in
- 8. International School of Engineering Plot 63/A, 1st Floor, Road # 13, Film Nagar, Jubilee Hills, Hyderabad - 500 033 For Individuals: +91-9502334561/62/63 For Corporates: +91-9618483483 Web: http://www.insofe.edu.in Facebook: https://www.facebook.com/insofe Twitter: https://twitter.com/Insofeedu YouTube: http://www.youtube.com/InsofeVideos Slide Share: http://www.slideshare.net/INSOFE LinkedIn: http://www.linkedin.com/company/internationalschool-of-engineering This presentation may contain references to findings of various reports available in the public domain. INSOFE makes no representation as to their accuracy or that the organization subscribes to those findings. The best place for students to learn Applied Engineering 8 http://www.insofe.edu.in