Выступление на августовском педсовете 28 августа 2014 года по теме "Анализ работы школы за 2013-2014 учебный год и задачи на следующий год". Докладчик Петракова Е.Н.
The document provides an overview of machine learning and decision tree learning. It discusses how machine learning can be applied to problems that are too difficult to program by hand, such as autonomous driving. It then describes decision tree learning, including how decision trees work, how the ID3 algorithm builds decision trees in a top-down manner by selecting the attribute that best splits the data at each step, and how decision trees can be converted to rules.
The document discusses the 12 attributes of a Safety Management System (SMS) as defined by the Helicopter Association International (HAI). It also discusses how SMS provides a systematic approach to accident prevention by focusing on the entire system of people and resources, rather than just individual frontline operators. Finally, it emphasizes that accidents are usually preventable if safety deficiencies are identified and addressed before an accident occurs through the use of integrated safety management systems.
El amarillo es un color primario que se encuentra en el espectro visible de la luz. Es el color entre el verde y el naranja en el arco iris y se asocia con la alegría, la felicidad, la creatividad, la inteligencia y la energía. El amarillo se utiliza comúnmente en la publicidad para atraer la atención.
Выступление на августовском педсовете 28 августа 2014 года по теме "Анализ работы школы за 2013-2014 учебный год и задачи на следующий год". Докладчик Петракова Е.Н.
The document provides an overview of machine learning and decision tree learning. It discusses how machine learning can be applied to problems that are too difficult to program by hand, such as autonomous driving. It then describes decision tree learning, including how decision trees work, how the ID3 algorithm builds decision trees in a top-down manner by selecting the attribute that best splits the data at each step, and how decision trees can be converted to rules.
The document discusses the 12 attributes of a Safety Management System (SMS) as defined by the Helicopter Association International (HAI). It also discusses how SMS provides a systematic approach to accident prevention by focusing on the entire system of people and resources, rather than just individual frontline operators. Finally, it emphasizes that accidents are usually preventable if safety deficiencies are identified and addressed before an accident occurs through the use of integrated safety management systems.
El amarillo es un color primario que se encuentra en el espectro visible de la luz. Es el color entre el verde y el naranja en el arco iris y se asocia con la alegría, la felicidad, la creatividad, la inteligencia y la energía. El amarillo se utiliza comúnmente en la publicidad para atraer la atención.
This document describes Lab 3 for an introduction to artificial intelligence course. The lab introduces decision trees for machine learning. Students will create a program to construct and evaluate binary decision trees on classification problems. The program must read data, divide it into training and test sets, build a decision tree from the training set, classify the test set with the tree and with prior probabilities, and report accuracy. Students will answer questions comparing decision trees to prior probabilities, examining the effect of training set size, and evaluating their own binary classification task.
This document provides an introduction and overview of programming microcontrollers using MikroBasic. It discusses why BASIC is a good choice, how to choose the right microcontroller, the basics of writing and compiling code in MikroBasic, and how to load the compiled program onto the microcontroller. The document is an excerpt from a book on MikroBasic that provides tutorials and examples for programming microcontrollers.
Virtualization allows operating systems and applications to run in virtual machines across physical hardware. This document discusses Microsoft's virtualization products from the datacenter to the desktop, including server, presentation, application, and desktop virtualization. It highlights benefits like accelerated provisioning, reduced costs, increased availability, and improved agility. It also provides examples of how organizations have benefited and reduced costs through server consolidation and eliminating application conflicts using virtualization.
ALADIN is an active learning system that uses machine learning to help security analysts identify anomalies and malware in network traffic logs. It combines active anomaly detection and active learning to minimize the amount of labeling an analyst needs to do. The system was tested on a real Microsoft network logs and was able to discover rare classes, maintain low error rates, and scale well while identifying network intrusions and malware.
This document provides a course syllabus for an introduction to artificial intelligence course. The 3-credit course introduces topics such as machine problem solving, game playing, knowledge representation, and machine learning. Evaluation is based on programming assignments, exams, and a grading scale. Required textbooks cover artificial intelligence foundations and applications. The course aims to provide an understanding of core AI topics and how computers solve knowledge problems.
This document discusses algorithm-independent machine learning techniques. It introduces concepts like bias and variance which can be used to quantify how well a learning algorithm matches a problem, regardless of the specific algorithm used. It discusses techniques like cross-validation, resampling, and combining multiple classifiers that can improve performance in a way that is independent of the learning algorithm. The document also covers principles like minimum description length and no free lunch which provide theoretical foundations for algorithm-independent machine learning.
This curriculum vitae summarizes the career and accomplishments of Dr. Yuan Yan Tang. Dr. Tang holds a Ph.D. in Computer Science and has held prestigious positions including Chair Professor and Dean. He has organized numerous international conferences and published over 300 papers. Dr. Tang's research focuses on machine learning, pattern recognition, and document analysis using techniques such as wavelet analysis.
AI – Week 21 Machine Learning: Macro Learningbutest
This document discusses machine learning techniques for planning, specifically macro learning. It defines macro learning as a process where a planner solves a problem and induces macros (compiled sequences of operators) from the solution. The planner can regress through states backwards to determine the weakest precondition of the operator sequence for achieving the goal. These macros can then be stored and reused to help solve future planning problems more efficiently by skipping search if the preconditions are met. However, learning too many macros can also increase the search space and time spent looking for applicable macros.
This literature review discusses location modelling and machine learning techniques for predicting user location and activity in smart environments. It covers ubiquitous computing sensors that collect data, location and activity modelling to store user data, and machine learning methods like Markov models and Bayesian techniques to predict future location and behaviors. Several key projects are also summarized that focus on determining location, predicting location and activities, and modelling entire environments for applications like smart meeting systems.
The document discusses machine learning techniques including using examples to teach a computer how to navigate a maze, filter spam emails, label images, synthesize textures and text, solve analogies, and model species habitats. It provides examples of machine learning applications like handwriting recognition, helicopter flight control, and the game 20 Questions. Key machine learning algorithms covered are spam filtering, which learns from labeled spam and ham emails to score new emails, and text synthesis, which generates new text by predicting the next word based on context in example text.
This document provides an introduction to text analysis within information retrieval and natural language processing. It discusses the history of text analysis and how early work led to advancements in computer-based text analysis in the 1950s. The document outlines two main approaches to text analysis - rule-based and statistical-based - and how each parses text through different analytical components and rules. Examples of how text analysis is used within information retrieval and natural language processing are also provided.
This document summarizes machine learning and inductive logic programming techniques for multi-agent systems. It discusses using machine learning for single agents and multi-agent systems, including inductive learning, reinforcement learning, and unsupervised learning. For multi-agent systems, it covers social awareness, communication, and role learning using techniques like Q-learning.
1. МОУ Лещановская СОШ Воробьевского района Воронежской области Медведева Людмила Ивановна Учитель высшей категории Использование информационно-коммуникационных технологий при обучении химии
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3. Использование ИКТ позволяет: Найти дополнительный источник информации Стремление реализовать себя, проявить свои возможности Проводить практические работы в условиях имитации Осуществлять самостоятельную исследовательскую деятельность при создании мультимедиа-презентации Учащимся работать более творчески и становиться уверенными в себе Осуществлять интегрированный подход в обучении .
5. Внеклассная работа по химии Массовая Групповая Индивидуальная Олимпиада Викторина Конференция Устный журнал Час химии Химический кружок Выпуск стенной газеты Изготовление стендов Выступление с докладами Защита рефератов Исследование и наблюдение Изготовление оборудования для хим.кабинета
6. Школа – это место, где учатся дети. Внеурочная деятельность учащихся.