I've put together some material about machine learning, big data, artificial intelligence, data mining, fuzzy logic and statistics trying to help to explain some of this miscellaneous technics that are changing the world.
This document discusses different machine learning algorithms including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Supervised learning uses labeled data to generate predictions, unsupervised learning finds patterns in unlabeled data through clustering and visualization, semi-supervised learning combines labeled and unlabeled data, and reinforcement learning uses rewards to learn behaviors. The document provides examples of applications for each type of learning such as price prediction, image clustering, and autonomous vehicles.
a) What is data.
b) types of data.
c) difference between data science and big data and data analytics.
d) relationship between data and artificial intelligence.
The document provides an introduction to machine learning including its history, components, classifications, and applications. It discusses key events in the history of machine learning from 1950 to 1985. It defines machine learning and describes how it works through algorithms and data to make autonomous decisions without human intervention. The main components of machine learning include gathering raw data, converting data into information, gathering knowledge from information, and using that knowledge to make decisions. The document also describes the main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Finally, it outlines several applications of machine learning such as traffic prediction, speech and image recognition, medical diagnosis, spam detection, and more.
Machine Learning. What is machine learning. Normal computer vs ML. Types of Machine Learning. Some ML Object detection methods. Faster CNN, RCNN, YOLO, SSD. Real Life ML Applications. Best Programming Languages for ML. Difference Between Machine Learning And Artificial Intelligence. Advantages of Machine Learning. Disadvantages of Machine Learning
The role of NLP & ML in Cognitive System by Sunantha Krishnansunanthakrishnan
Cognitive computing uses machine learning techniques to solve problems by detecting patterns in large amounts of data, generating hypotheses, and continuously learning. It represents a new approach for creating applications that can support business and research goals. The three fundamental principles of cognitive systems are that they learn from training and observation, create models to learn from, and generate testable hypotheses based on evidence and data. Natural language processing is key to interpreting unstructured text data and allowing cognitive systems to understand language, extract meaning, and answer questions.
These slides are from a presentation on understanding Machine Learning at a high level. The talk touches on linear regression, neural networks, and how Deep Learning fits into Machine Learning.
Machine learning is a subset of artificial intelligence that allows computer systems to learn from data without being explicitly programmed. It involves the use of algorithms to recognize patterns in data in order to make predictions or decisions without being explicitly programmed to perform the specific tasks. There are two main types of machine learning: supervised learning which uses labeled data to predict outputs, and unsupervised learning which finds hidden patterns in unlabeled data. Machine learning has many applications and enables organizations to analyze complex data automatically to make data-driven decisions.
Machine learning techniques can be used to enable computers to learn from data and perform tasks. Some key techniques discussed in the document include decision tree learning, artificial neural networks, Bayesian learning, support vector machines, genetic algorithms, graph-based learning, reinforcement learning, and pattern recognition. Each technique has its own strengths and applications.
This document discusses different machine learning algorithms including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Supervised learning uses labeled data to generate predictions, unsupervised learning finds patterns in unlabeled data through clustering and visualization, semi-supervised learning combines labeled and unlabeled data, and reinforcement learning uses rewards to learn behaviors. The document provides examples of applications for each type of learning such as price prediction, image clustering, and autonomous vehicles.
a) What is data.
b) types of data.
c) difference between data science and big data and data analytics.
d) relationship between data and artificial intelligence.
The document provides an introduction to machine learning including its history, components, classifications, and applications. It discusses key events in the history of machine learning from 1950 to 1985. It defines machine learning and describes how it works through algorithms and data to make autonomous decisions without human intervention. The main components of machine learning include gathering raw data, converting data into information, gathering knowledge from information, and using that knowledge to make decisions. The document also describes the main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Finally, it outlines several applications of machine learning such as traffic prediction, speech and image recognition, medical diagnosis, spam detection, and more.
Machine Learning. What is machine learning. Normal computer vs ML. Types of Machine Learning. Some ML Object detection methods. Faster CNN, RCNN, YOLO, SSD. Real Life ML Applications. Best Programming Languages for ML. Difference Between Machine Learning And Artificial Intelligence. Advantages of Machine Learning. Disadvantages of Machine Learning
The role of NLP & ML in Cognitive System by Sunantha Krishnansunanthakrishnan
Cognitive computing uses machine learning techniques to solve problems by detecting patterns in large amounts of data, generating hypotheses, and continuously learning. It represents a new approach for creating applications that can support business and research goals. The three fundamental principles of cognitive systems are that they learn from training and observation, create models to learn from, and generate testable hypotheses based on evidence and data. Natural language processing is key to interpreting unstructured text data and allowing cognitive systems to understand language, extract meaning, and answer questions.
These slides are from a presentation on understanding Machine Learning at a high level. The talk touches on linear regression, neural networks, and how Deep Learning fits into Machine Learning.
Machine learning is a subset of artificial intelligence that allows computer systems to learn from data without being explicitly programmed. It involves the use of algorithms to recognize patterns in data in order to make predictions or decisions without being explicitly programmed to perform the specific tasks. There are two main types of machine learning: supervised learning which uses labeled data to predict outputs, and unsupervised learning which finds hidden patterns in unlabeled data. Machine learning has many applications and enables organizations to analyze complex data automatically to make data-driven decisions.
Machine learning techniques can be used to enable computers to learn from data and perform tasks. Some key techniques discussed in the document include decision tree learning, artificial neural networks, Bayesian learning, support vector machines, genetic algorithms, graph-based learning, reinforcement learning, and pattern recognition. Each technique has its own strengths and applications.
Lu Zhang is a 33-year-old male Chinese national with a PhD in neural physiology from East China Normal University. He has over 5 years of experience as a postdoctoral researcher studying signal processing in neuroscience and psychiatry. His research interests include data mining in brain science using machine learning methods and neuronal mechanisms of brain oscillations. He is fluent in English and his native Chinese.
This document discusses using machine learning and deep learning techniques for sentiment analysis on social media data. It proposes a system to classify tweets as having positive, negative or neutral sentiment. The system involves data acquisition from Twitter, preprocessing tweets, extracting features, and applying machine learning classifiers like SVR, Random Forest and Decision Tree. It aims to analyze public sentiment on topics and help organizations understand people's views.
Meetup sthlm - introduction to Machine Learning with demo casesZenodia Charpy
This document provides an agenda and overview of topics related to data science and machine learning. It discusses data science processes including data preparation, algorithm selection, model deployment, and performance measurement. It also distinguishes machine learning from artificial intelligence and describes common machine learning algorithms like supervised and unsupervised learning. Examples of supervised and unsupervised learning applications are presented along with generic workflows. Machine learning algorithm selection and example cases are also summarized.
Student intervention detection using deep learning techniqueVenkat Projects
The document discusses using an artificial neural network (ANN) and deep learning techniques to detect issues in student performance data. It provides background on ANNs, describing their three-layer structure and learning abilities. It then details using Python packages like Keras, TensorFlow and scikit-learn to build and train an ANN model on a student dataset to perform student intervention detection. Screenshots are included showing the uploading of data and exploration, preprocessing, model generation and results.
This document provides an overview of machine learning applications across several domains:
- Financial applications including trading strategies, forecasting, and portfolio management utilize techniques like reinforcement learning and neural networks.
- Weather forecasting uses neural networks, support vector machines, and time series analysis to predict temperature and rainfall.
- Speech recognition and natural language processing apply machine learning to tasks like document classification, tagging, and parsing using probabilistic and neural network models.
- Other applications include smart environments using predictive models, computer games using reinforcement learning, robotics combining mechanics and software, and medical decision support analyzing clinical and biological data.
This document discusses using machine learning algorithms to detect malware files. It introduces different types of malware and provides an overview of machine learning methods for malware detection, including random forests, gradient boosting, and adaboost. The objectives are to use machine learning to detect legitimate and malware files and achieve high testing accuracy. The dataset includes over 130,000 files labeled as legitimate or malware. Several algorithms are applied including decision trees, random forests and gradient boosting. Random forests achieved the highest accuracy of 99.35% at distinguishing between legitimate and malware files.
This document provides an overview of machine learning. It defines machine learning as a form of artificial intelligence that allows systems to automatically learn and improve from experience without being explicitly programmed. The document then discusses why machine learning is important, how it works by exploring data and identifying patterns with minimal human intervention, and provides examples of machine learning applications like autonomous vehicles. It also summarizes the main types of machine learning: supervised learning, unsupervised learning, reinforcement learning, and deep learning. Finally, it distinguishes machine learning from deep learning and defines data science.
Machine learning is the ability of machines to learn from experience and improve their performance on tasks over time without being explicitly programmed. It involves the development of algorithms that allow computers to learn from large amounts of data. There are different types of machine learning including supervised learning, unsupervised learning, and semi-supervised learning. The history of machine learning began in the 1950s with research into neural networks, pattern recognition, and knowledge systems. Significant developments occurred in each subsequent decade, including decision trees, connectionism, reinforcement learning, and support vector machines. Machine learning continues to progress and find new applications in areas like data mining, language processing, and robotics.
Introduction to generative adversarial networks (GANs)chauhankapil
Generative adversarial networks (GANs) are a type of deep learning model used for generative modeling. GANs involve training two neural networks - a generator and a discriminator. The generator produces synthetic data while the discriminator evaluates it as real or fake. This adversarial process trains the generator to produce increasingly realistic samples. GANs frame generative modeling as an adversarial game to learn the training data distribution in an unsupervised manner.
An inference engine is a component of artificial intelligence systems that derives answers from a knowledge base by reasoning about the information contained within it to formulate new conclusions. Inference engines work in either forward chaining mode, which starts with known facts to assert new facts, or backward chaining mode, which starts with goals and works backward to determine what facts need to be asserted to achieve those goals.
Machine learning is a subfield of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" with data, without being explicitly programmed. The goal is to build models that predict outcomes accurately from large amounts of data. There are two primary machine learning methods: supervised learning, where the computer is provided labeled training data to learn from, and unsupervised learning, where the computer must find hidden patterns in unlabeled data. Common machine learning tasks include classification, prediction, clustering, and association.
Machine learning works by processing data to discover patterns that can be used to analyze new data. Popular programming languages for machine learning include Python, R, and SQL. There are several types of machine learning including supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and deep learning. Common machine learning tasks involve classification, regression, clustering, dimensionality reduction, and model selection. Machine learning is widely used for applications such as spam filtering, recommendations, speech recognition, and machine translation.
Machine learning is a branch of artificial intelligence concerned with using algorithms to learn from data and improve automatically through experience without being explicitly programmed. The algorithms build a mathematical model of sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are often categorized as supervised or unsupervised. Supervised learning involves predicting the value of a target variable based on input variables whereas unsupervised learning identifies hidden patterns or grouping in the data.
How to create your own artificial neural networksAgrata Shukla
See how to create your own neural networks.Artificial neural networks are used to simulate the functioning of the human brain.The machine could not think but it predicts.These ANN’s are inspired from the nervous system of the human brain.
This document provides an introduction to machine learning and data science. It discusses key concepts like supervised vs. unsupervised learning, classification algorithms, overfitting and underfitting data. It also addresses challenges like having bad quality or insufficient training data. Python and MATLAB are introduced as suitable software for machine learning projects.
Soft computing is an emerging approach to computing that aims to model human-like decision making through techniques like fuzzy logic, neural networks, and genetic algorithms. It allows for imprecision, uncertainty, and approximation to achieve practical and robust solutions. Soft computing deals with problems that are too complex or undefined to model mathematically. It is well-suited for real-world problems where ideal solutions do not exist.
The document discusses different machine learning techniques including fuzzy logic, artificial neural networks, genetic algorithms, and case-based reasoning. It provides examples of how each technique works and potential applications. It notes that while these methods can be useful, they also have limitations such as lack of explainability and reliability issues for complex systems.
Lu Zhang is a 33-year-old male Chinese national with a PhD in neural physiology from East China Normal University. He has over 5 years of experience as a postdoctoral researcher studying signal processing in neuroscience and psychiatry. His research interests include data mining in brain science using machine learning methods and neuronal mechanisms of brain oscillations. He is fluent in English and his native Chinese.
This document discusses using machine learning and deep learning techniques for sentiment analysis on social media data. It proposes a system to classify tweets as having positive, negative or neutral sentiment. The system involves data acquisition from Twitter, preprocessing tweets, extracting features, and applying machine learning classifiers like SVR, Random Forest and Decision Tree. It aims to analyze public sentiment on topics and help organizations understand people's views.
Meetup sthlm - introduction to Machine Learning with demo casesZenodia Charpy
This document provides an agenda and overview of topics related to data science and machine learning. It discusses data science processes including data preparation, algorithm selection, model deployment, and performance measurement. It also distinguishes machine learning from artificial intelligence and describes common machine learning algorithms like supervised and unsupervised learning. Examples of supervised and unsupervised learning applications are presented along with generic workflows. Machine learning algorithm selection and example cases are also summarized.
Student intervention detection using deep learning techniqueVenkat Projects
The document discusses using an artificial neural network (ANN) and deep learning techniques to detect issues in student performance data. It provides background on ANNs, describing their three-layer structure and learning abilities. It then details using Python packages like Keras, TensorFlow and scikit-learn to build and train an ANN model on a student dataset to perform student intervention detection. Screenshots are included showing the uploading of data and exploration, preprocessing, model generation and results.
This document provides an overview of machine learning applications across several domains:
- Financial applications including trading strategies, forecasting, and portfolio management utilize techniques like reinforcement learning and neural networks.
- Weather forecasting uses neural networks, support vector machines, and time series analysis to predict temperature and rainfall.
- Speech recognition and natural language processing apply machine learning to tasks like document classification, tagging, and parsing using probabilistic and neural network models.
- Other applications include smart environments using predictive models, computer games using reinforcement learning, robotics combining mechanics and software, and medical decision support analyzing clinical and biological data.
This document discusses using machine learning algorithms to detect malware files. It introduces different types of malware and provides an overview of machine learning methods for malware detection, including random forests, gradient boosting, and adaboost. The objectives are to use machine learning to detect legitimate and malware files and achieve high testing accuracy. The dataset includes over 130,000 files labeled as legitimate or malware. Several algorithms are applied including decision trees, random forests and gradient boosting. Random forests achieved the highest accuracy of 99.35% at distinguishing between legitimate and malware files.
This document provides an overview of machine learning. It defines machine learning as a form of artificial intelligence that allows systems to automatically learn and improve from experience without being explicitly programmed. The document then discusses why machine learning is important, how it works by exploring data and identifying patterns with minimal human intervention, and provides examples of machine learning applications like autonomous vehicles. It also summarizes the main types of machine learning: supervised learning, unsupervised learning, reinforcement learning, and deep learning. Finally, it distinguishes machine learning from deep learning and defines data science.
Machine learning is the ability of machines to learn from experience and improve their performance on tasks over time without being explicitly programmed. It involves the development of algorithms that allow computers to learn from large amounts of data. There are different types of machine learning including supervised learning, unsupervised learning, and semi-supervised learning. The history of machine learning began in the 1950s with research into neural networks, pattern recognition, and knowledge systems. Significant developments occurred in each subsequent decade, including decision trees, connectionism, reinforcement learning, and support vector machines. Machine learning continues to progress and find new applications in areas like data mining, language processing, and robotics.
Introduction to generative adversarial networks (GANs)chauhankapil
Generative adversarial networks (GANs) are a type of deep learning model used for generative modeling. GANs involve training two neural networks - a generator and a discriminator. The generator produces synthetic data while the discriminator evaluates it as real or fake. This adversarial process trains the generator to produce increasingly realistic samples. GANs frame generative modeling as an adversarial game to learn the training data distribution in an unsupervised manner.
An inference engine is a component of artificial intelligence systems that derives answers from a knowledge base by reasoning about the information contained within it to formulate new conclusions. Inference engines work in either forward chaining mode, which starts with known facts to assert new facts, or backward chaining mode, which starts with goals and works backward to determine what facts need to be asserted to achieve those goals.
Machine learning is a subfield of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" with data, without being explicitly programmed. The goal is to build models that predict outcomes accurately from large amounts of data. There are two primary machine learning methods: supervised learning, where the computer is provided labeled training data to learn from, and unsupervised learning, where the computer must find hidden patterns in unlabeled data. Common machine learning tasks include classification, prediction, clustering, and association.
Machine learning works by processing data to discover patterns that can be used to analyze new data. Popular programming languages for machine learning include Python, R, and SQL. There are several types of machine learning including supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and deep learning. Common machine learning tasks involve classification, regression, clustering, dimensionality reduction, and model selection. Machine learning is widely used for applications such as spam filtering, recommendations, speech recognition, and machine translation.
Machine learning is a branch of artificial intelligence concerned with using algorithms to learn from data and improve automatically through experience without being explicitly programmed. The algorithms build a mathematical model of sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are often categorized as supervised or unsupervised. Supervised learning involves predicting the value of a target variable based on input variables whereas unsupervised learning identifies hidden patterns or grouping in the data.
How to create your own artificial neural networksAgrata Shukla
See how to create your own neural networks.Artificial neural networks are used to simulate the functioning of the human brain.The machine could not think but it predicts.These ANN’s are inspired from the nervous system of the human brain.
This document provides an introduction to machine learning and data science. It discusses key concepts like supervised vs. unsupervised learning, classification algorithms, overfitting and underfitting data. It also addresses challenges like having bad quality or insufficient training data. Python and MATLAB are introduced as suitable software for machine learning projects.
Soft computing is an emerging approach to computing that aims to model human-like decision making through techniques like fuzzy logic, neural networks, and genetic algorithms. It allows for imprecision, uncertainty, and approximation to achieve practical and robust solutions. Soft computing deals with problems that are too complex or undefined to model mathematically. It is well-suited for real-world problems where ideal solutions do not exist.
The document discusses different machine learning techniques including fuzzy logic, artificial neural networks, genetic algorithms, and case-based reasoning. It provides examples of how each technique works and potential applications. It notes that while these methods can be useful, they also have limitations such as lack of explainability and reliability issues for complex systems.
This document discusses applying a neural network approach to decision making in a self-organizing computing network (SOCN). It proposes using concepts from fuzzy logic and neural networks to build a computing network that can handle mixed data types, like symbolic and numeric data. The network would have input, hidden, and output layers connected by transfer functions. The hidden cells would self-organize based on training data to learn relationships between input and output cells. This approach aims to allow the network to make decisions on data sets with diverse attribute types in a more effective way than other techniques.
Soft computing is an approach to computing that aims to model human-like decision making. It deals with imprecise or uncertain data using techniques like fuzzy logic, neural networks, and genetic algorithms. The goal is to develop systems that are tolerant of imprecision, uncertainty, and approximation to achieve practical and low-cost solutions to real-world problems. Soft computing was initiated in 1981 and includes fields like fuzzy logic, neural networks, and evolutionary computation. It provides approximate solutions using techniques like neural network reasoning, genetic programming, and functional approximation.
This document provides an introduction to artificial intelligence and discusses key concepts in the field. It explores what constitutes an intelligent system, references important milestones in AI like the Turing Test, and examines examples of AI applications such as chess-playing systems and pattern recognition. The document also discusses techniques used in AI research, including artificial neural networks and fuzzy logic. It poses questions about the capabilities and limitations of machine intelligence and investigates approaches to developing intelligent systems.
Machine learning is a scientific discipline that develops algorithms to allow systems to learn from data and improve automatically without being explicitly programmed. The document discusses several key machine learning concepts including supervised learning algorithms like decision trees and Naive Bayes classification. Decision trees use branching to represent classification or regression rules learned from data to make predictions. Naive Bayes classification is a simple probabilistic classifier that applies Bayes' theorem with strong independence assumptions between features.
Join us for an enlightening session on AI/ML by Jeevanshi Sharma, an MS graduate from the University of Alberta with accolades from Outreachy'22 and MITACS GRI'21. Delve into cutting-edge advancements, applications, and ethical considerations. Learn basic steps to start your ML journey and explore industry applications, advancements, and associated careers.
This document provides an overview of artificial intelligence, including its history, key concepts like neural networks and fuzzy logic, characteristics such as problem solving and learning, applications in fields like finance and medicine, and programming languages commonly used in AI like Lisp and Prolog. While AI has made impressive advances, it has yet to achieve human-level intelligence and abilities like autonomous learning from diverse, real-world experiences. Future progress may require better integration of existing techniques.
This document provides an overview of machine learning basics, including definitions of machine learning, neural networks, and different types of machine learning such as supervised, unsupervised, and reinforcement learning. It discusses applications of machine learning in areas like healthcare, finance, translation, and gaming. Deep learning and challenges in the field are also summarized. The document is intended as a brief introduction for beginners to understand machine learning concepts.
This document provides an introduction to an online course on artificial intelligence (AI). It discusses that the course is offered through Udacity over 4 months and is instructed by Peter Norvig and Sebastian Thrun. The summary provides an overview of the key topics that will be covered in the course, including machine learning techniques like supervised and unsupervised learning, applications of AI such as computer vision and natural language processing, and how AI is used in areas like video games, music, and intelligent personal assistants.
Introduction to Artificial IntelligenceLuca Bianchi
Artificial intelligence has been defined in many ways as our understanding has evolved. Currently, AI is divided into narrow, general and super intelligence based on capabilities. Machine learning is a key approach in AI and involves algorithms that can learn from data to improve performance. Deep learning uses neural networks with many layers to learn representations of data and has achieved success in areas like computer vision and natural language processing.
Supervised Machine Learning Techniques common algorithms and its applicationTara ram Goyal
The document provides an introduction to supervised machine learning, including definitions, techniques, and applications. It discusses how supervised machine learning involves training algorithms using labeled input data to make predictions on unlabeled data. Some common supervised learning algorithms mentioned are naive Bayes, decision trees, linear regression, support vector machines, and neural networks. Applications discussed include self-driving cars, online recommendations, fraud detection, and spam filtering. The key difference between supervised and unsupervised learning is that supervised learning uses labeled training data while unsupervised learning does not have pre-existing labels.
The document provides an overview of artificial intelligence, including definitions, key concepts, and applications. It defines AI as the simulation of human intelligence in machines, and notes the differences between weak/narrow AI which focuses on specific problems, versus strong/general AI which aims to achieve human-level intelligence. The document also discusses how AI works by trying to think and act well, and by attempting to think and act like humans. It provides examples of AI application areas and practical tools used today.
This document provides biographical information about Şaban Dalaman and summaries of key concepts in artificial intelligence and machine learning. It summarizes Şaban Dalaman's educational and professional background, then discusses Alan Turing's universal machine concept, the 1956 Dartmouth workshop proposal that helped define the field of AI, and definitions of AI, machine learning, deep learning, and data science. It also lists different tribes and algorithms within machine learning.
Artificial intelligence uses in productive systems and impacts on the world...Fernando Alcoforado
This essay aims to present the scientific and technological advances of artificial intelligence, their uses in productive systems and their impacts in the world of work.
This document provides information about an Artificial Intelligence course. The key details are:
- The course is CSC 343, taught over 3 lecture hours and 2 lab hours
Ali Akram Saber's document discusses intelligent urban traffic control systems using various artificial intelligence techniques. It covers neural networks, genetic algorithms, expert systems, fuzzy logic, and rule-based systems. Neural networks can be separated into models, networks, and learning rules. Genetic algorithms mimic natural selection to find solutions. Expert systems contain knowledge bases and reasoning engines. Rule-based systems separate knowledge from execution. Fuzzy logic handles approximate reasoning between true and false values.
This presentation deals with the basics of AI and it's connection with neural network. Additionally, it explains the pros and cons of AI along with the applications.
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of May 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
Generative Classifiers: Classifying with Bayesian decision theory, Bayes’ rule, Naïve Bayes classifier.
Discriminative Classifiers: Logistic Regression, Decision Trees: Training and Visualizing a Decision Tree, Making Predictions, Estimating Class Probabilities, The CART Training Algorithm, Attribute selection measures- Gini impurity; Entropy, Regularization Hyperparameters, Regression Trees, Linear Support vector machines.
5. Statistics and logics
Classical logic only permits
conclusions of true or false.
Fuzzy logic evaluate ranges
between 0 and 1 defining degrees of
truth.
6. Statistics and logics (cont.)
Frequentist: 50% of probability to be full and 50% of probability to be empty.
Bayesian: The probability will be check after estimate probability of to be drunk
regarding drinker mood.
Fuzzy: The glass is full and empty at sametime. With more pertinence for full if the
drinker is in good mood or more pertinence for empty if the drinker is in bad mood.
Bordering values for Classic Math Bordering values for Fuzzy Logic
Conclusion
8. Fuzzy logic (Artificial Intelligence)
Similar to human thinking and
behavior for decision making.
Virtual game environments,
imprecision and aleatory.
Chatbot messaging,
textual interactions.
9. Neural networks
Clusters of data are used to understand the relationships and correlation levels to take actions. It's similar
to the way of our cerebral neurons and genetic evolution works, from there the terms: Neuro network
and Genetic algorithms.
It can be used for example to represent the probabilistic relationships between diseases and symptoms. Given
symptoms, the network can be used to compute the probabilities of the presence of various diseases.
This techniques/algorithms are divided in:
Supervised (naive bayes or fuzzy rules e.g.) parameters and answers for training.
Unsupervised (K-means p.e.) non-parametric and/or no right/wrong answers.
Normally you use many mixing subject knowledge and data availability.
10. Use of self-learning algorithms to predict action through network
informations (bigdata e.g.).
“Against data there’s no arguments”
(math try to predict but only real-time data can capture reality)
Neural networks (Machine Learning)
11. Neurofuzzy network
(knowledge or datamining)
Easier way to communicate linguistic and math together. Each layer of neurons has specific
functions to capture specialists knowledge and automatic data analysis for learning.
12. Data mining
Use of statistics to find correlation weight
between variables creating networks and
levels of importance.