This document provides an introduction to probabilistic and Bayesian analytics through a series of slides from a lecture by Andrew W. Moore. It begins by discussing the uncertainty in the world and how probability provides a framework to model uncertainty. The fundamentals of probability are then reviewed, including discrete random variables, probabilities, the axioms of probability, and theorems that can be derived from the axioms. Conditional probability and Bayesian inference are introduced. Joint probability distributions are discussed as a way to specify probabilities over multiple variables. The document aims to provide the foundations for understanding probabilistic modeling and reasoning.
2013-1 Machine Learning Lecture 03 - Andrew Moore - probabilistic and baye…Dongseo University
This document is a slide presentation on probabilistic and Bayesian analytics given by Andrew W. Moore. The presentation covers fundamental probability concepts like random variables, the axioms of probability, and conditional probability. It also demonstrates how to use Bayes' rule to calculate conditional probabilities and make probabilistic inferences. As an example, it shows how Bayes' rule can be applied to determine how much to pay for an envelope when some information is revealed about its contents. The presentation aims to provide an introduction to probabilistic reasoning concepts.
2013-1 Machine Learning Lecture 03 - Andrew Moore - bayes nets for represe…Dongseo University
This document contains slides from a lecture on Bayes networks given by Andrew W. Moore. The slides:
- Introduce Bayes networks as a methodology for building joint distributions in manageable chunks and addressing the problem of using joint distributions.
- Cover background topics on probability theory including random variables, conditional probability, Bayes' rule, and joint distributions.
- Explain that Bayes networks allow representing and reasoning about uncertainty, with practical applications in fields like medicine.
- Suggest Bayes networks are one of the most important technologies to emerge in machine learning and AI for expressing certainty and uncertainty.
This document discusses maximum likelihood estimation for learning Gaussian distributions from data. It begins with an overview of maximum likelihood estimation and learning univariate and multivariate Gaussians. It then derives the maximum likelihood estimates for the mean and variance of a univariate Gaussian distribution. The maximum likelihood estimate for the mean is simply the sample mean, and the estimate for the variance is related to the sum of squared deviations from the sample mean.
Leveraging social technologies and particularly social workflow is a core part of how organizations today can manage the transition to a new way of working or usher in a more holistic cultural change.
Frank Nack discusses audio and emotion recognition from speech. The document covers listening to sounds, producing sounds, and interpreting emotions from acoustic variables in speech like pitch, intensity, speech rate, and voice quality. It also discusses challenges with speech data collection and segmentation, feature extraction, and using classifiers like SVM, neural networks, decision trees, and HMM for emotion recognition. Measurement and benchmarking of audio emotion recognition systems is difficult due to varying conditions and datasets.
The document describes various features of a dating application, including:
1) Features that allow users to view and filter matches, send messages and invites, and search for best matches.
2) Profile editing and validation tools, as well as status updates on dates.
3) Private messaging and rooms, gifts, and video uploading for marketing profiles.
4) Match history, blocking, and speed dating options to facilitate dating.
Release your potential with Maconomy Essentials Cloud ERPStefan Kim (Grahn)
Generic ERP systems are too expensive, take too long to implement, don't address real business issues, and the providers don't understand the business. Maconomy Essentials is presented as an alternative that can be implemented within 2-4 calendar weeks, is purpose-built for project-centric businesses, and helps them release their potential through key business insights. It enables growth in revenue, profitability, utilization and cash flow through an integrated solution addressing clients, projects, people, financials and business metrics.
2013-1 Machine Learning Lecture 03 - Andrew Moore - probabilistic and baye…Dongseo University
This document is a slide presentation on probabilistic and Bayesian analytics given by Andrew W. Moore. The presentation covers fundamental probability concepts like random variables, the axioms of probability, and conditional probability. It also demonstrates how to use Bayes' rule to calculate conditional probabilities and make probabilistic inferences. As an example, it shows how Bayes' rule can be applied to determine how much to pay for an envelope when some information is revealed about its contents. The presentation aims to provide an introduction to probabilistic reasoning concepts.
2013-1 Machine Learning Lecture 03 - Andrew Moore - bayes nets for represe…Dongseo University
This document contains slides from a lecture on Bayes networks given by Andrew W. Moore. The slides:
- Introduce Bayes networks as a methodology for building joint distributions in manageable chunks and addressing the problem of using joint distributions.
- Cover background topics on probability theory including random variables, conditional probability, Bayes' rule, and joint distributions.
- Explain that Bayes networks allow representing and reasoning about uncertainty, with practical applications in fields like medicine.
- Suggest Bayes networks are one of the most important technologies to emerge in machine learning and AI for expressing certainty and uncertainty.
This document discusses maximum likelihood estimation for learning Gaussian distributions from data. It begins with an overview of maximum likelihood estimation and learning univariate and multivariate Gaussians. It then derives the maximum likelihood estimates for the mean and variance of a univariate Gaussian distribution. The maximum likelihood estimate for the mean is simply the sample mean, and the estimate for the variance is related to the sum of squared deviations from the sample mean.
Leveraging social technologies and particularly social workflow is a core part of how organizations today can manage the transition to a new way of working or usher in a more holistic cultural change.
Frank Nack discusses audio and emotion recognition from speech. The document covers listening to sounds, producing sounds, and interpreting emotions from acoustic variables in speech like pitch, intensity, speech rate, and voice quality. It also discusses challenges with speech data collection and segmentation, feature extraction, and using classifiers like SVM, neural networks, decision trees, and HMM for emotion recognition. Measurement and benchmarking of audio emotion recognition systems is difficult due to varying conditions and datasets.
The document describes various features of a dating application, including:
1) Features that allow users to view and filter matches, send messages and invites, and search for best matches.
2) Profile editing and validation tools, as well as status updates on dates.
3) Private messaging and rooms, gifts, and video uploading for marketing profiles.
4) Match history, blocking, and speed dating options to facilitate dating.
Release your potential with Maconomy Essentials Cloud ERPStefan Kim (Grahn)
Generic ERP systems are too expensive, take too long to implement, don't address real business issues, and the providers don't understand the business. Maconomy Essentials is presented as an alternative that can be implemented within 2-4 calendar weeks, is purpose-built for project-centric businesses, and helps them release their potential through key business insights. It enables growth in revenue, profitability, utilization and cash flow through an integrated solution addressing clients, projects, people, financials and business metrics.
Probability density in data mining and coverianceudhayax793
This document discusses probability densities in data mining. It begins with an introduction to probability density functions (PDFs) and why they are important for modeling real-valued data. It then covers notation and properties of univariate and multivariate continuous PDFs, including expectations, variances, and independence. Examples are provided to illustrate concepts such as interpreting the value of a PDF and sampling from a distribution. The document is intended as a tutorial on probability densities for data mining applications.
This document discusses Bayes networks for representing and reasoning about uncertainty. It begins by noting the benefits of using joint distributions to describe uncertain worlds but also the problem of using joint distributions due to their complexity. Bayes networks allow building joint distributions in manageable chunks by representing conditional independence relationships between variables. The document then discusses representing uncertainty using probability and key concepts in probability such as conditional probability, Bayes' rule, and working through examples to demonstrate their application.
This document provides an introduction to probabilistic and Bayesian analytics through a series of slides from a lecture by Andrew W. Moore. The key points covered include:
- Probability is used to represent uncertainty and is quantified by the fraction of possible worlds where an event occurs.
- The axioms of probability are introduced and interpreted visually, including that probabilities must be between 0 and 1 and the addition rule for mutually exclusive events.
- Important theorems are derived from the axioms, such as the probability of the complement of an event.
- Conditional probability is defined as the probability of one event given another using a visual representation.
- Bayes' rule for updating probabilities based on new information is
1. The document discusses uncertainty and different methods for handling it, including probability theory.
2. It explains that probability can be used to represent an agent's degree of belief in a proposition given available evidence.
3. Key concepts covered include prior and conditional probability, Bayes' rule, and independence assumptions which allow reducing the size of probability models.
This document discusses cross-validation techniques for evaluating machine learning models on a dataset and preventing overfitting. It introduces linear regression, quadratic regression, and join-the-dots/nonparametric regression on a sample regression problem. It then explains the test set method for model evaluation but notes its high variance. Leave-one-out cross-validation (LOOCV) and k-fold cross-validation are presented as alternatives that make more efficient use of data. Examples are given comparing the performance of different models using these cross-validation techniques on the sample regression problem. The document concludes by discussing how cross-validation can be used for model selection tasks like choosing the number of hidden units in a neural network or the k value in
- Cosmology relies heavily on statistics and probability to analyze astronomical data and test theories of the universe.
- Bayesian probability provides a rigorous way to assign probabilities to hypotheses based on prior knowledge and new data, and update beliefs.
- The universe appears "fine tuned" for life with parameter values that allow complexity; Bayesian reasoning can help assess if these are surprising coincidences.
- The concordance model of cosmology posits that initial fluctuations in the early universe formed a Gaussian random field, but some anomalies in cosmic microwave background data could indicate "weirdness" beyond this simple picture.
This document provides an overview of discrete random variables and expectation. It defines key concepts like probability mass function (pmf), cumulative distribution function (cdf), and expected value. Examples of common discrete distributions like Bernoulli, binomial, and geometric are presented. The memoryless property of geometric distributions is discussed. Computing expected values using definitions and properties is demonstrated. Two word problems about accepting gambles based on expected value are given to interpret the meaning of expectation.
- A survey found that 45% of students visit Tapijn park to relax, 27% visit both Tapijn park and the city center, and 40% do not visit the city center.
- The probability that a student visits Tapijn park given they visit the city center (P(A)) is 0.45.
- The probability that a student visits Tapijn park or the city center (P(B)) is 0.78.
The document discusses uncertainty and probabilistic reasoning. It describes sources of uncertainty like partial information, unreliable information, and conflicting information from multiple sources. It then discusses representing and reasoning with uncertainty using techniques like default logic, rules with probabilities, and probability theory. The key approaches covered are conditional probability, independence, conditional independence, and using Bayes' rule to update probabilities based on new evidence.
- This document outlines the syllabus for a course on generative models and naive Bayes classifiers.
- It discusses the projects, exams, and administration of the course. Students will complete a poster presentation and video, and exams will cover all material from the course in a cumulative manner.
- The document also provides a brief recap of error-driven learning approaches like discriminative and generative models for classification tasks.
This document discusses exercises related to information gain and decision tree learning. Exercise 2 calculates the information gain of attributes a1 and a2 on a sample dataset. Exercise 3 discusses overfitting related to using a unique identifier attribute. Exercise 4 shows that an attribute with many unique values can achieve maximum information gain but may not be a good predictor. Exercise 5 discusses approaches for handling missing values when calculating information gain.
This document discusses neural networks and their applications. It covers perceptrons, which are single-layer neural networks, and the perceptron training rule. It also describes gradient descent search and the delta rule for training neural networks. The document introduces multi-layer neural networks and the backpropagation algorithm for training these more complex networks. In the end, it provides examples of applications of neural networks such as text-to-speech, fraud detection, and game playing.
This document provides instructions for 5 exercises on data mining homework. Students are asked to submit their answers to the given exercises electronically by November 25, 2010. The exercises cover topics such as information gain, handling missing attribute values, perceptrons, gradient descent, and stochastic gradient descent. Contact information is provided for two teaching assistants in case students have any questions.
This document discusses the need for benchmarking and evaluation of visualization tools for data mining. It proposes developing standardized test datasets and metrics to compare different visualization approaches. The challenges include:
1) Performance depends on user expertise - domain knowledge is needed to understand complex real-world datasets. Evaluations must account for different user skill levels.
2) Perceptual issues - comparisons require controlling display/viewing conditions and ensuring users receive comparable training to learn how to interpret visualizations.
3) Acceptance by the KDD community - overcoming technical and cultural barriers to establishing benchmarking as a standard practice. The document advocates developing a centralized testing laboratory to standardize evaluations.
The document describes a study investigating how collaborative creativity can be supported electronically while maintaining face-to-face communication. The researchers designed a brainstorming application using an interactive table and wall display, and compared it to traditional paper-based brainstorming. They derived design guidelines for collaborative systems in interactive environments based on considerations from the application's design and observations during a user study with 30 participants. The guidelines aim to support group awareness, minimize cognitive load, and mediate mutual idea activation in order to foster collaborative creative problem solving.
The document describes the process of constructing decision trees. It begins with an example weather dataset and shows how to build a decision tree to predict whether to play or not based on attributes like outlook, temperature, etc. It then discusses the key steps in constructing decision trees which include selecting the best attribute to split on at each node based on information gain. It also discusses overfitting and the need for tree pruning. The document provides formulas to calculate information gain and discusses strategies like using a chi-squared test to select statistically robust splits during tree construction.
This document outlines linear regression, which is a machine learning technique for predicting real-valued outputs based on numerical input variables. It assumes a linear relationship between the inputs and outputs. Linear regression finds the linear equation that best fits the training data by minimizing a sum of squared errors function. The parameters of the linear equation can be estimated analytically through differentiation and solving for when the partial derivatives are equal to zero.
Probability density in data mining and coverianceudhayax793
This document discusses probability densities in data mining. It begins with an introduction to probability density functions (PDFs) and why they are important for modeling real-valued data. It then covers notation and properties of univariate and multivariate continuous PDFs, including expectations, variances, and independence. Examples are provided to illustrate concepts such as interpreting the value of a PDF and sampling from a distribution. The document is intended as a tutorial on probability densities for data mining applications.
This document discusses Bayes networks for representing and reasoning about uncertainty. It begins by noting the benefits of using joint distributions to describe uncertain worlds but also the problem of using joint distributions due to their complexity. Bayes networks allow building joint distributions in manageable chunks by representing conditional independence relationships between variables. The document then discusses representing uncertainty using probability and key concepts in probability such as conditional probability, Bayes' rule, and working through examples to demonstrate their application.
This document provides an introduction to probabilistic and Bayesian analytics through a series of slides from a lecture by Andrew W. Moore. The key points covered include:
- Probability is used to represent uncertainty and is quantified by the fraction of possible worlds where an event occurs.
- The axioms of probability are introduced and interpreted visually, including that probabilities must be between 0 and 1 and the addition rule for mutually exclusive events.
- Important theorems are derived from the axioms, such as the probability of the complement of an event.
- Conditional probability is defined as the probability of one event given another using a visual representation.
- Bayes' rule for updating probabilities based on new information is
1. The document discusses uncertainty and different methods for handling it, including probability theory.
2. It explains that probability can be used to represent an agent's degree of belief in a proposition given available evidence.
3. Key concepts covered include prior and conditional probability, Bayes' rule, and independence assumptions which allow reducing the size of probability models.
This document discusses cross-validation techniques for evaluating machine learning models on a dataset and preventing overfitting. It introduces linear regression, quadratic regression, and join-the-dots/nonparametric regression on a sample regression problem. It then explains the test set method for model evaluation but notes its high variance. Leave-one-out cross-validation (LOOCV) and k-fold cross-validation are presented as alternatives that make more efficient use of data. Examples are given comparing the performance of different models using these cross-validation techniques on the sample regression problem. The document concludes by discussing how cross-validation can be used for model selection tasks like choosing the number of hidden units in a neural network or the k value in
- Cosmology relies heavily on statistics and probability to analyze astronomical data and test theories of the universe.
- Bayesian probability provides a rigorous way to assign probabilities to hypotheses based on prior knowledge and new data, and update beliefs.
- The universe appears "fine tuned" for life with parameter values that allow complexity; Bayesian reasoning can help assess if these are surprising coincidences.
- The concordance model of cosmology posits that initial fluctuations in the early universe formed a Gaussian random field, but some anomalies in cosmic microwave background data could indicate "weirdness" beyond this simple picture.
This document provides an overview of discrete random variables and expectation. It defines key concepts like probability mass function (pmf), cumulative distribution function (cdf), and expected value. Examples of common discrete distributions like Bernoulli, binomial, and geometric are presented. The memoryless property of geometric distributions is discussed. Computing expected values using definitions and properties is demonstrated. Two word problems about accepting gambles based on expected value are given to interpret the meaning of expectation.
- A survey found that 45% of students visit Tapijn park to relax, 27% visit both Tapijn park and the city center, and 40% do not visit the city center.
- The probability that a student visits Tapijn park given they visit the city center (P(A)) is 0.45.
- The probability that a student visits Tapijn park or the city center (P(B)) is 0.78.
The document discusses uncertainty and probabilistic reasoning. It describes sources of uncertainty like partial information, unreliable information, and conflicting information from multiple sources. It then discusses representing and reasoning with uncertainty using techniques like default logic, rules with probabilities, and probability theory. The key approaches covered are conditional probability, independence, conditional independence, and using Bayes' rule to update probabilities based on new evidence.
- This document outlines the syllabus for a course on generative models and naive Bayes classifiers.
- It discusses the projects, exams, and administration of the course. Students will complete a poster presentation and video, and exams will cover all material from the course in a cumulative manner.
- The document also provides a brief recap of error-driven learning approaches like discriminative and generative models for classification tasks.
This document discusses exercises related to information gain and decision tree learning. Exercise 2 calculates the information gain of attributes a1 and a2 on a sample dataset. Exercise 3 discusses overfitting related to using a unique identifier attribute. Exercise 4 shows that an attribute with many unique values can achieve maximum information gain but may not be a good predictor. Exercise 5 discusses approaches for handling missing values when calculating information gain.
This document discusses neural networks and their applications. It covers perceptrons, which are single-layer neural networks, and the perceptron training rule. It also describes gradient descent search and the delta rule for training neural networks. The document introduces multi-layer neural networks and the backpropagation algorithm for training these more complex networks. In the end, it provides examples of applications of neural networks such as text-to-speech, fraud detection, and game playing.
This document provides instructions for 5 exercises on data mining homework. Students are asked to submit their answers to the given exercises electronically by November 25, 2010. The exercises cover topics such as information gain, handling missing attribute values, perceptrons, gradient descent, and stochastic gradient descent. Contact information is provided for two teaching assistants in case students have any questions.
This document discusses the need for benchmarking and evaluation of visualization tools for data mining. It proposes developing standardized test datasets and metrics to compare different visualization approaches. The challenges include:
1) Performance depends on user expertise - domain knowledge is needed to understand complex real-world datasets. Evaluations must account for different user skill levels.
2) Perceptual issues - comparisons require controlling display/viewing conditions and ensuring users receive comparable training to learn how to interpret visualizations.
3) Acceptance by the KDD community - overcoming technical and cultural barriers to establishing benchmarking as a standard practice. The document advocates developing a centralized testing laboratory to standardize evaluations.
The document describes a study investigating how collaborative creativity can be supported electronically while maintaining face-to-face communication. The researchers designed a brainstorming application using an interactive table and wall display, and compared it to traditional paper-based brainstorming. They derived design guidelines for collaborative systems in interactive environments based on considerations from the application's design and observations during a user study with 30 participants. The guidelines aim to support group awareness, minimize cognitive load, and mediate mutual idea activation in order to foster collaborative creative problem solving.
The document describes the process of constructing decision trees. It begins with an example weather dataset and shows how to build a decision tree to predict whether to play or not based on attributes like outlook, temperature, etc. It then discusses the key steps in constructing decision trees which include selecting the best attribute to split on at each node based on information gain. It also discusses overfitting and the need for tree pruning. The document provides formulas to calculate information gain and discusses strategies like using a chi-squared test to select statistically robust splits during tree construction.
This document outlines linear regression, which is a machine learning technique for predicting real-valued outputs based on numerical input variables. It assumes a linear relationship between the inputs and outputs. Linear regression finds the linear equation that best fits the training data by minimizing a sum of squared errors function. The parameters of the linear equation can be estimated analytically through differentiation and solving for when the partial derivatives are equal to zero.
This document summarizes a lecture on decision tree learning. It introduces decision trees and algorithms like ID3 for building trees from data. Key concepts discussed include information gain, overfitting, pruning trees, handling continuous attributes, and predicting continuous values with regression trees. Decision trees are built by recursively splitting the training data on attributes that maximize information gain until reaching leaf nodes with class predictions.
Christof Monz gave a lecture on probabilities and information theory for a data mining class. He provided a quick refresher on key probability concepts like sample spaces, events, and probability functions. He discussed examples of calculating probabilities for coin tosses and dice rolls. Monz also covered entropy as a measure of uncertainty and how more optimal encoding can achieve lower entropy. Finally, he included a brief review of calculus concepts like derivatives that are relevant to data mining.
The document summarizes the key topics from the first lecture of a data mining course. It introduces data mining as the process of extracting implicit and potentially useful information from large amounts of data. It discusses why data mining is needed due to the abundance of data and challenges of manual organization. The lecture then covers machine learning techniques used for tasks like classification, clustering, and prediction. It provides examples of data mining applications and outlines the typical steps involved in a machine learning approach.
This document contains instructions for homework assignments in data mining. It includes 3 exercises:
1) Describe two scenarios where data mining could be applied, what would be predicted, relevant attributes, data used, and potential problems.
2) Derive Bayes' rule step-by-step from definitions of conditional probability and other rules.
3) Calculate entropy for variables with different probability distributions, find the minimum bits needed to represent values, and explain which distributions have highest and lowest entropy.
This chapter discusses subjectivism as an alternative to objectivism for providing a theoretical foundation for information management. Subjectivism focuses on human sense-making and interpretation rather than objective truths. The chapter argues that subjectivism fails to address economic value, a key concern for organizations. It suggests combining objectivism and subjectivism into an integrated approach. Subjectivism is illustrated using practice-based social theories, which view social practices as transcending the divide between objectivism and subjectivism. However, differences between the two philosophies remain fundamental.
Groups tend to focus discussion on information that is commonly known, neglecting unique information known to only some members. This can result in suboptimal decisions. Groups also tend to accentuate their initial views, leading to more extreme decisions than individuals would make alone. Highly cohesive groups may prioritize consensus over considering information that challenges group unity. Effective information management is needed to help groups make better use of all relevant information in their decision making.
This chapter discusses how information management has been strongly influenced by the philosophical tradition of objectivism. Objectivism views the world as consisting of distinct objects that exist independently of human cognition and can be studied to gain objective knowledge. It has shaped key definitions and goals in information management, such as defining information and knowledge as granules that represent objective realities. Information management also shows influence from microeconomics, viewing information exchange as a market and aiming to maximize participation and competition. However, the chapter argues that objectivism may not provide the best foundation for information management, as it cannot adequately deal with the subjective nature of information.
The document discusses text and images as visual sign systems for representing knowledge. It provides conceptual models for representing text, including models for typography, layout, writing systems, syntax, dictionaries, semantics, style and genre. Text representation relies on agreed upon codes and rules. Images are represented using different codes, including perceptual, textual, social, and syntagmatic/paradigmatic codes. Both text and images can be described using standards like XML, RDF and MPEG for interpretation and understanding.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
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
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Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
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.
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 Kanvaria
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
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.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
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