The document discusses assessments of student learning outcomes for the Computer Science department at the University of Maryland. Small committees were formed to evaluate courses and projects based on criteria outlined in the learning outcomes.
For learning outcome #1, a committee reviewed student code from introductory programming courses to assess coding skills. Most students demonstrated proficiency in basic programming concepts.
For learning outcome #2, a committee evaluated students' ability to prove mathematical concepts by examining answers to an exam question. 75% of students received a rating of excellent or very good.
For learning outcome #3, a committee reviewed a programming project from CMSC 412 and found two of three projects examined demonstrated clear, well-documented code with good debugging
Machine Learning: Foundations Course Number 0368403401butest
This machine learning foundations course will consist of 4 homework assignments, both theoretical and programming problems in Matlab. There will be a final exam. Students will work in groups of 2-3 to take notes during classes in LaTeX format. These class notes will contribute 30% to the overall grade. The course will cover basic machine learning concepts like storage and retrieval, learning rules, estimating flexible models, and applications in areas like control, medical diagnosis, and document retrieval.
The document discusses the exponential growth of biomedical research data and literature. It describes challenges researchers face in keeping up with the vast amount of information. Text mining techniques can help by automatically extracting relevant information and facts from literature and organizing them into structured knowledgebases. Named entity recognition is an important text mining task that involves identifying mentions of biomedical entities in text. Both rule-based and machine learning approaches have been used for named entity recognition.
This document discusses using data mining techniques like machine learning to analyze air quality data and generate models for predicting pollution levels. It summarizes applying decision trees and neural networks to data on pollutants and weather factors in Cambridge, UK. The models showed air temperature as the dominant predictor of ozone levels. While data mining provided insights, the author notes it is most useful complementing existing scientific domain knowledge and physical models of air quality.
Semi-supervised learning uses both labeled and unlabeled data for training. There are three main paradigms: transductive learning which considers the test set, active learning which allows the learner to query an oracle, and multi-view learning which uses two independent feature sets. Co-training is an algorithm that uses multi-view learning and semi-supervised learning by training two classifiers on different views and having each label unlabeled data for the other. It assumes the views are sufficient and conditionally independent given the label.
This document summarizes an experiment comparing different multi-class classification methods using binary SVMs on various datasets. It evaluates 1-vs-1, 1-vs-many, and ECOC classification schemes using SVMLight on UCI and KDD intrusion detection datasets. It reports the accuracy and runtime of each method on different datasets. Parameter tuning was challenging for some datasets, and the full KDD dataset was too large to test with. Overall, ECOC generally had the best accuracy, followed by 1-vs-1 and then 1-vs-many classification.
The document discusses the author's experience with open source software development and its interaction with institutional contexts in research and higher education. The author has a long history developing open source software for applied statistics and geospatial applications using R. He notes a mismatch between institutional preferences for secrecy and open source practices of mutual trust and community-building. The author was an early contributor to the R project in 1997 and discusses the evolution of R from its beginnings as an academic initiative to the large open source community that exists today.
The document discusses assessments of student learning outcomes for the Computer Science department at the University of Maryland. Small committees were formed to evaluate courses and projects based on criteria outlined in the learning outcomes.
For learning outcome #1, a committee reviewed student code from introductory programming courses to assess coding skills. Most students demonstrated proficiency in basic programming concepts.
For learning outcome #2, a committee evaluated students' ability to prove mathematical concepts by examining answers to an exam question. 75% of students received a rating of excellent or very good.
For learning outcome #3, a committee reviewed a programming project from CMSC 412 and found two of three projects examined demonstrated clear, well-documented code with good debugging
Machine Learning: Foundations Course Number 0368403401butest
This machine learning foundations course will consist of 4 homework assignments, both theoretical and programming problems in Matlab. There will be a final exam. Students will work in groups of 2-3 to take notes during classes in LaTeX format. These class notes will contribute 30% to the overall grade. The course will cover basic machine learning concepts like storage and retrieval, learning rules, estimating flexible models, and applications in areas like control, medical diagnosis, and document retrieval.
The document discusses the exponential growth of biomedical research data and literature. It describes challenges researchers face in keeping up with the vast amount of information. Text mining techniques can help by automatically extracting relevant information and facts from literature and organizing them into structured knowledgebases. Named entity recognition is an important text mining task that involves identifying mentions of biomedical entities in text. Both rule-based and machine learning approaches have been used for named entity recognition.
This document discusses using data mining techniques like machine learning to analyze air quality data and generate models for predicting pollution levels. It summarizes applying decision trees and neural networks to data on pollutants and weather factors in Cambridge, UK. The models showed air temperature as the dominant predictor of ozone levels. While data mining provided insights, the author notes it is most useful complementing existing scientific domain knowledge and physical models of air quality.
Semi-supervised learning uses both labeled and unlabeled data for training. There are three main paradigms: transductive learning which considers the test set, active learning which allows the learner to query an oracle, and multi-view learning which uses two independent feature sets. Co-training is an algorithm that uses multi-view learning and semi-supervised learning by training two classifiers on different views and having each label unlabeled data for the other. It assumes the views are sufficient and conditionally independent given the label.
This document summarizes an experiment comparing different multi-class classification methods using binary SVMs on various datasets. It evaluates 1-vs-1, 1-vs-many, and ECOC classification schemes using SVMLight on UCI and KDD intrusion detection datasets. It reports the accuracy and runtime of each method on different datasets. Parameter tuning was challenging for some datasets, and the full KDD dataset was too large to test with. Overall, ECOC generally had the best accuracy, followed by 1-vs-1 and then 1-vs-many classification.
The document discusses the author's experience with open source software development and its interaction with institutional contexts in research and higher education. The author has a long history developing open source software for applied statistics and geospatial applications using R. He notes a mismatch between institutional preferences for secrecy and open source practices of mutual trust and community-building. The author was an early contributor to the R project in 1997 and discusses the evolution of R from its beginnings as an academic initiative to the large open source community that exists today.
This document is a request for proposal from the Georgia Ports Authority for security systems at several ports using Department of Homeland Security grant funds. It includes three main projects: 1) The Savannah River Intrusion Network to monitor the Savannah River with cameras, 2) The Colonel's Island Intrusion Network to protect GPA assets in Brunswick with cameras, and 3) TWIC Access System Integration to prepare terminals for Transportation Worker Identification Credentials. Bidders are requested to provide options and pricing for various security camera networks, access control systems, and command and control integration components. The RFP provides project background, requirements, timelines, and terms for the bidding process.
The document contains three unrelated articles:
1) A summary of an Afghan National Army artillery company training with Marines to improve their accuracy and coordination.
2) Details on gas-free engineering and confined space entry procedures to identify and mitigate atmospheric hazards aboard Navy ships.
3) Old Dominion University being included in a ranking of top military-friendly universities and colleges.
This document provides a summary of Mohak Shah's education, research interests, and professional experience. It summarizes that Mohak Shah is a researcher in machine learning and natural language processing at the University of Ottawa with a PhD in Computer Science. His research interests include machine learning theory, applications in NLP and bioinformatics. He has published extensively in refereed conferences and journals.
The poem expresses the importance of showing love for loved ones every day because tomorrow is not promised. It describes wishing you had spent more time with a loved one if you knew it was the last time you would see them. The poem encourages readers to take extra time today to express love, forgiveness and gratitude to loved ones in case "tomorrow never comes."
Knowledge mining is a process of extracting patterns and relationships from databases to improve decision-making. It has various applications including business intelligence, product design, manufacturing, and research profiling. The document then provides three examples: 1) A financial company used knowledge mining to analyze customer data and target marketing promotions. 2) Engineers have used it to help design products by matching requirements to existing part designs. 3) Researchers have used text mining to map relationships between topics in academic literature and identify active individuals and organizations.
This document outlines a machine learning project to recognize handwritten characters using Bayesian linear regression. The goal is to develop models that can map image features to class membership values. Students are instructed to:
1. Define target outputs for training data using k-nearest neighbors.
2. Build Bayesian regression models to map inputs to the target outputs and estimate hyperparameters.
3. Evaluate models on a test set both qualitatively by examining outputs and quantitatively by comparing character/non-character histograms.
4. Write a 5-10 page report summarizing methodology, results, observations and relating findings back to concepts from the textbook.
This paper describes using a genetic algorithm to teach a simulated three-legged creature to walk. The creature, called a Tripod, lives in a 3D physics simulation. Its goal is to travel as far as possible within 30 seconds. A genetic algorithm varies the Tripod's joint movements and selects those that perform best, as measured by distance traveled, to be passed on to the next generation. While genetic algorithms are useful for problems like this where the underlying functions are unknown, they are computationally intensive and cannot guarantee optimal solutions. The paper discusses challenges in analyzing the genetic algorithm's convergence and tuning its parameters for this complex, non-deterministic problem domain.
Optimizing Intelligent Agents Constraint Satisfaction with ...butest
This document discusses using neural networks to optimize an intelligent agent's constraint satisfaction module for assigning sailors to new jobs. Various neural network techniques are evaluated, including single-layer perceptron, multilayer perceptron, and support vector machine models. The data comes from Navy databases and expert surveys. The neural networks are trained on data representing sailors, jobs, and constraints. The best performing models are multilayer perceptron and support vector machine with Adatron algorithms, which accurately classify jobs that experts decided should be offered to sailors.
Los sistemas binarios representan números y datos usando solo los dígitos 0 y 1. Para convertir entre sistemas binarios y decimales, se debe sumar los valores posicionales de cada dígito binario utilizando la base 2 en lugar de la base 10.
The AgentMatcher system matches learners and learning objects (LOs) using a tree-structured representation of metadata. It extracts metadata from LOs using LOMGen and stores it in a database. Learners can enter query parameters as a weighted tree, which is compared to LO metadata trees to find similar LOs. Top matches above a similarity threshold are returned to the learner. LOMGen semi-automatically generates metadata using keywords and allows an administrator to refine selections. This enhances precision over simple keyword searches.
The document discusses computational thinking and its applications across many disciplines. It defines computational thinking as the process of abstraction in computing, including defining relationships between layers of abstraction. The document provides examples of computational thinking in fields like biology, physics, chemistry, mathematics, engineering, and more. It also discusses the importance of computational thinking in education and daily life.
This document discusses machine intelligence and machine learning. It covers topics such as behavior-based AI vs knowledge-based AI, supervised vs unsupervised learning, classification vs prediction, and decision tree induction for classification. Decision trees are built using an algorithm that selects the attribute that best splits the data at each step to create partitions. Pruning techniques are used to avoid overfitting.
The Central Analytical Facility (CAF) annual report summarizes activities from August 2008 to August 2009. The CAF provides major research instrumentation and training to support teaching, research, and service at the University of Alabama. It houses 8 instruments and had over 100 trained users. The report describes personnel changes, finances, usage levels, and plans to increase productivity and acquire new instruments to support cutting-edge research across disciplines.
The document discusses machine learning techniques for finding patterns in data and using those patterns to make predictions. It covers topics like classification algorithms, decision trees, neural networks, learning as a search process, and how machine learning systems use bias to avoid overfitting training data. Examples are provided on classifying weather data to determine if a baseball game should be played, classifying iris flowers, predicting CPU performance, and diagnosing soybean diseases.
This document discusses extracting protein interactions from biomedical literature. It notes that while databases contain some known interactions, much information remains in unstructured text across millions of published articles. The document presents an example Medline abstract describing interactions between cyclin A, cyclin D1, CDK subunits, and the Rb protein. It aims to develop automated methods to locate and structure such interaction information at scale from literature.
This document appears to be a catalog listing various relaxing mechanical chairs for sale, including models called Dondolò, Athena, Day, Giulia, Giulia XL, Pocket, Memory, Mizar, and Aida. Each chair listing includes a repeated link to the website www.tinomariani.it, suggesting this is the manufacturer or retailer of the chairs.
The document describes several leather couches and sectional sofas available from the furniture company Tinomariani. It lists the product names such as "Central small", "Chic", "Blow", "Winter", and "Chester" along with the company website.
This document contains the names of various furniture collections and designers, including Divani Moderni, Merit, Blow, Harry, Madison, York, Mixage, and Isola, as well as multiple references to the website www.tinomariani.it.
This document is a request for proposal from the Georgia Ports Authority for security systems at several ports using Department of Homeland Security grant funds. It includes three main projects: 1) The Savannah River Intrusion Network to monitor the Savannah River with cameras, 2) The Colonel's Island Intrusion Network to protect GPA assets in Brunswick with cameras, and 3) TWIC Access System Integration to prepare terminals for Transportation Worker Identification Credentials. Bidders are requested to provide options and pricing for various security camera networks, access control systems, and command and control integration components. The RFP provides project background, requirements, timelines, and terms for the bidding process.
The document contains three unrelated articles:
1) A summary of an Afghan National Army artillery company training with Marines to improve their accuracy and coordination.
2) Details on gas-free engineering and confined space entry procedures to identify and mitigate atmospheric hazards aboard Navy ships.
3) Old Dominion University being included in a ranking of top military-friendly universities and colleges.
This document provides a summary of Mohak Shah's education, research interests, and professional experience. It summarizes that Mohak Shah is a researcher in machine learning and natural language processing at the University of Ottawa with a PhD in Computer Science. His research interests include machine learning theory, applications in NLP and bioinformatics. He has published extensively in refereed conferences and journals.
The poem expresses the importance of showing love for loved ones every day because tomorrow is not promised. It describes wishing you had spent more time with a loved one if you knew it was the last time you would see them. The poem encourages readers to take extra time today to express love, forgiveness and gratitude to loved ones in case "tomorrow never comes."
Knowledge mining is a process of extracting patterns and relationships from databases to improve decision-making. It has various applications including business intelligence, product design, manufacturing, and research profiling. The document then provides three examples: 1) A financial company used knowledge mining to analyze customer data and target marketing promotions. 2) Engineers have used it to help design products by matching requirements to existing part designs. 3) Researchers have used text mining to map relationships between topics in academic literature and identify active individuals and organizations.
This document outlines a machine learning project to recognize handwritten characters using Bayesian linear regression. The goal is to develop models that can map image features to class membership values. Students are instructed to:
1. Define target outputs for training data using k-nearest neighbors.
2. Build Bayesian regression models to map inputs to the target outputs and estimate hyperparameters.
3. Evaluate models on a test set both qualitatively by examining outputs and quantitatively by comparing character/non-character histograms.
4. Write a 5-10 page report summarizing methodology, results, observations and relating findings back to concepts from the textbook.
This paper describes using a genetic algorithm to teach a simulated three-legged creature to walk. The creature, called a Tripod, lives in a 3D physics simulation. Its goal is to travel as far as possible within 30 seconds. A genetic algorithm varies the Tripod's joint movements and selects those that perform best, as measured by distance traveled, to be passed on to the next generation. While genetic algorithms are useful for problems like this where the underlying functions are unknown, they are computationally intensive and cannot guarantee optimal solutions. The paper discusses challenges in analyzing the genetic algorithm's convergence and tuning its parameters for this complex, non-deterministic problem domain.
Optimizing Intelligent Agents Constraint Satisfaction with ...butest
This document discusses using neural networks to optimize an intelligent agent's constraint satisfaction module for assigning sailors to new jobs. Various neural network techniques are evaluated, including single-layer perceptron, multilayer perceptron, and support vector machine models. The data comes from Navy databases and expert surveys. The neural networks are trained on data representing sailors, jobs, and constraints. The best performing models are multilayer perceptron and support vector machine with Adatron algorithms, which accurately classify jobs that experts decided should be offered to sailors.
Los sistemas binarios representan números y datos usando solo los dígitos 0 y 1. Para convertir entre sistemas binarios y decimales, se debe sumar los valores posicionales de cada dígito binario utilizando la base 2 en lugar de la base 10.
The AgentMatcher system matches learners and learning objects (LOs) using a tree-structured representation of metadata. It extracts metadata from LOs using LOMGen and stores it in a database. Learners can enter query parameters as a weighted tree, which is compared to LO metadata trees to find similar LOs. Top matches above a similarity threshold are returned to the learner. LOMGen semi-automatically generates metadata using keywords and allows an administrator to refine selections. This enhances precision over simple keyword searches.
The document discusses computational thinking and its applications across many disciplines. It defines computational thinking as the process of abstraction in computing, including defining relationships between layers of abstraction. The document provides examples of computational thinking in fields like biology, physics, chemistry, mathematics, engineering, and more. It also discusses the importance of computational thinking in education and daily life.
This document discusses machine intelligence and machine learning. It covers topics such as behavior-based AI vs knowledge-based AI, supervised vs unsupervised learning, classification vs prediction, and decision tree induction for classification. Decision trees are built using an algorithm that selects the attribute that best splits the data at each step to create partitions. Pruning techniques are used to avoid overfitting.
The Central Analytical Facility (CAF) annual report summarizes activities from August 2008 to August 2009. The CAF provides major research instrumentation and training to support teaching, research, and service at the University of Alabama. It houses 8 instruments and had over 100 trained users. The report describes personnel changes, finances, usage levels, and plans to increase productivity and acquire new instruments to support cutting-edge research across disciplines.
The document discusses machine learning techniques for finding patterns in data and using those patterns to make predictions. It covers topics like classification algorithms, decision trees, neural networks, learning as a search process, and how machine learning systems use bias to avoid overfitting training data. Examples are provided on classifying weather data to determine if a baseball game should be played, classifying iris flowers, predicting CPU performance, and diagnosing soybean diseases.
This document discusses extracting protein interactions from biomedical literature. It notes that while databases contain some known interactions, much information remains in unstructured text across millions of published articles. The document presents an example Medline abstract describing interactions between cyclin A, cyclin D1, CDK subunits, and the Rb protein. It aims to develop automated methods to locate and structure such interaction information at scale from literature.
This document appears to be a catalog listing various relaxing mechanical chairs for sale, including models called Dondolò, Athena, Day, Giulia, Giulia XL, Pocket, Memory, Mizar, and Aida. Each chair listing includes a repeated link to the website www.tinomariani.it, suggesting this is the manufacturer or retailer of the chairs.
The document describes several leather couches and sectional sofas available from the furniture company Tinomariani. It lists the product names such as "Central small", "Chic", "Blow", "Winter", and "Chester" along with the company website.
This document contains the names of various furniture collections and designers, including Divani Moderni, Merit, Blow, Harry, Madison, York, Mixage, and Isola, as well as multiple references to the website www.tinomariani.it.
The document appears to contain product listings for various sofas or couches. It includes dimensions for the sofas such as length, height, and depth both when closed and opened. The listings also include colors or names for the sofas like "pasha", "zeus", "energy", and "double".
Este catálogo apresenta uma variedade de sofás e bancos modulares e combináveis da marca Bertosalotti. Inclui modelos como Soft Bench, Soft Bench Tessuto, Casablanca, Thomas e Jack que permitem diferentes configurações através de peças que se encaixam umas nas outras.
O documento lista vários modelos de sofás e bancos da marca Divani Moderni, incluindo Soft Bench, Casablanca, Jack, Thomas, Espace, Bold e Johnny, sendo que alguns são compatíveis entre si para compor conjuntos.