Advanced topics in artificial neural networksswapnac12
The document discusses various advanced topics in artificial neural networks including alternative error functions, error minimization procedures, recurrent networks, and dynamically modifying network structure. It describes adding penalty terms to the error function to reduce weights and overfitting, using line search and conjugate gradient methods for error minimization, how recurrent networks can capture dependencies over time, and algorithms for growing or pruning network complexity like cascade correlation.
The document discusses sequential covering algorithms for learning rule sets from data. It describes how sequential covering algorithms work by iteratively learning one rule at a time to cover examples, removing covered examples, and repeating until all examples are covered. It also discusses variations of this approach, including using a general-to-specific beam search to learn each rule and alternatives like the AQ algorithm that learn rules to cover specific target values. Finally, it describes how first-order logic can be used to learn more general rules than propositional logic by representing relationships between attributes.
Introduction to Artificial Neural NetworksAdri Jovin
This presentation describes the various components, classification and application of Artificial Neural Networks. It also gives an outline on the other soft computing techniques also.
Distributed deadlock occurs when processes are blocked while waiting for resources held by other processes in a distributed system without a central coordinator. There are four conditions for deadlock: mutual exclusion, hold and wait, non-preemption, and circular wait. Deadlock can be addressed by ignoring it, detecting and resolving occurrences, preventing conditions through constraints, or avoiding it through careful resource allocation. Detection methods include centralized coordination of resource graphs or distributed probe messages to identify resource waiting cycles. Prevention strategies impose timestamp or age-based priority to resource requests to eliminate cycles.
The Dempster-Shafer Theory was developed by Arthur Dempster in 1967 and Glenn Shafer in 1976 as an alternative to Bayesian probability. It allows one to combine evidence from different sources and obtain a degree of belief (or probability) for some event. The theory uses belief functions and plausibility functions to represent degrees of belief for various hypotheses given certain evidence. It was developed to describe ignorance and consider all possible outcomes, unlike Bayesian probability which only considers single evidence. An example is given of using the theory to determine the murderer in a room with 4 people where the lights went out.
A Bayesian network is a probabilistic graphical model that represents conditional dependencies among random variables using a directed acyclic graph. It consists of nodes representing variables and directed edges representing causal relationships. Each node contains a conditional probability table that quantifies the effect of its parent nodes on that variable. Bayesian networks can be used to calculate the probability of events occurring based on the network structure and conditional probability tables, such as computing the probability of an alarm sounding given that no burglary or earthquake occurred but two neighbors called.
Using prior knowledge to initialize the hypothesis,kbannswapnac12
1) The KBANN algorithm uses a domain theory represented as Horn clauses to initialize an artificial neural network before training it with examples. This helps the network generalize better than random initialization when training data is limited.
2) KBANN constructs a network matching the domain theory's predictions exactly, then refines it with backpropagation to fit examples. This balances theory and data when they disagree.
3) In experiments on promoter recognition, KBANN achieved a 4% error rate compared to 8% for backpropagation alone, showing the benefit of prior knowledge.
Support vector machines (SVM) are a supervised learning method used for classification and regression analysis. SVMs find a hyperplane that maximizes the margin between two classes of objects. They can handle non-linear classification problems by projecting data into a higher dimensional space. The training points closest to the separating hyperplane are called support vectors. SVMs learn the discrimination boundary between classes rather than modeling each class individually.
Advanced topics in artificial neural networksswapnac12
The document discusses various advanced topics in artificial neural networks including alternative error functions, error minimization procedures, recurrent networks, and dynamically modifying network structure. It describes adding penalty terms to the error function to reduce weights and overfitting, using line search and conjugate gradient methods for error minimization, how recurrent networks can capture dependencies over time, and algorithms for growing or pruning network complexity like cascade correlation.
The document discusses sequential covering algorithms for learning rule sets from data. It describes how sequential covering algorithms work by iteratively learning one rule at a time to cover examples, removing covered examples, and repeating until all examples are covered. It also discusses variations of this approach, including using a general-to-specific beam search to learn each rule and alternatives like the AQ algorithm that learn rules to cover specific target values. Finally, it describes how first-order logic can be used to learn more general rules than propositional logic by representing relationships between attributes.
Introduction to Artificial Neural NetworksAdri Jovin
This presentation describes the various components, classification and application of Artificial Neural Networks. It also gives an outline on the other soft computing techniques also.
Distributed deadlock occurs when processes are blocked while waiting for resources held by other processes in a distributed system without a central coordinator. There are four conditions for deadlock: mutual exclusion, hold and wait, non-preemption, and circular wait. Deadlock can be addressed by ignoring it, detecting and resolving occurrences, preventing conditions through constraints, or avoiding it through careful resource allocation. Detection methods include centralized coordination of resource graphs or distributed probe messages to identify resource waiting cycles. Prevention strategies impose timestamp or age-based priority to resource requests to eliminate cycles.
The Dempster-Shafer Theory was developed by Arthur Dempster in 1967 and Glenn Shafer in 1976 as an alternative to Bayesian probability. It allows one to combine evidence from different sources and obtain a degree of belief (or probability) for some event. The theory uses belief functions and plausibility functions to represent degrees of belief for various hypotheses given certain evidence. It was developed to describe ignorance and consider all possible outcomes, unlike Bayesian probability which only considers single evidence. An example is given of using the theory to determine the murderer in a room with 4 people where the lights went out.
A Bayesian network is a probabilistic graphical model that represents conditional dependencies among random variables using a directed acyclic graph. It consists of nodes representing variables and directed edges representing causal relationships. Each node contains a conditional probability table that quantifies the effect of its parent nodes on that variable. Bayesian networks can be used to calculate the probability of events occurring based on the network structure and conditional probability tables, such as computing the probability of an alarm sounding given that no burglary or earthquake occurred but two neighbors called.
Using prior knowledge to initialize the hypothesis,kbannswapnac12
1) The KBANN algorithm uses a domain theory represented as Horn clauses to initialize an artificial neural network before training it with examples. This helps the network generalize better than random initialization when training data is limited.
2) KBANN constructs a network matching the domain theory's predictions exactly, then refines it with backpropagation to fit examples. This balances theory and data when they disagree.
3) In experiments on promoter recognition, KBANN achieved a 4% error rate compared to 8% for backpropagation alone, showing the benefit of prior knowledge.
Support vector machines (SVM) are a supervised learning method used for classification and regression analysis. SVMs find a hyperplane that maximizes the margin between two classes of objects. They can handle non-linear classification problems by projecting data into a higher dimensional space. The training points closest to the separating hyperplane are called support vectors. SVMs learn the discrimination boundary between classes rather than modeling each class individually.
The document discusses analytical learning methods like explanation-based learning. It explains that analytical learning uses prior knowledge and deductive reasoning to augment training examples, allowing it to generalize better than methods relying solely on data. Explanation-based learning analyzes examples according to prior knowledge to infer relevant features. The document provides examples of using explanation-based learning to learn chess concepts and safe stacking of objects. It also describes the PROLOG-EBG algorithm for explanation-based learning.
Medium Access Control :-
1.Distributed Operation
2.Synchronization
3.Hidden Terminals
4.Exposed terminals
5.Throughput
6.Access delay
7.Fairness
8.Real-time Traffic support
9.Resource reservation
10.Ability to measure resource availability
11.Capability for power control
Adaptive rate control
Use of directional antennas
Self-organizing networks can perform unsupervised clustering by mapping high-dimensional input patterns into a smaller number of clusters in output space through competitive learning. Fixed weight competitive networks like Maxnet, Mexican Hat net, and Hamming net use competitive learning with fixed weights. Maxnet uses winner-take-all competition to select the neuron whose weights best match the input. Mexican Hat net has both excitatory and inhibitory connections between neurons to enhance contrast. Hamming net determines which exemplar vector most closely matches the input using the Hamming distance measure.
The document discusses geo-cast routing protocols, which deliver data packets to nodes within a specified geographic region. It describes two categories of geo-cast protocols: data-transmission oriented protocols, which focus on transmitting information from source to geographic region, and routing creation oriented protocols, which aim to reduce flooding overhead while maintaining delivery accuracy. Specific protocols discussed include Location-Based Multicast, Geo-GRID, Geo-TORA, and mesh-based geo-cast routing. The document concludes by noting open issues like scalability, applications, addressing, and security for geo-cast routing over mobile ad hoc networks.
Boosting techniques like AdaBoost combine the predictions of many weak learner models to create a stronger joint model. AdaBoost uses stumps, or decision trees with one node and two leaves, as the weak learners. It adjusts the weights of samples to focus on incorrectly classified samples. Over many iterations, it boosts the weights of harder to classify samples to improve predictive performance compared to a single weak learner.
FOIL is an algorithm for inductive logic programming that learns sets of first-order rules from examples to perform tasks like predicting relationships between people. FOIL extends earlier rule learning algorithms to handle first-order logic representations using predicates, variables, and quantification. It searches for the most specific rules that cover positive training examples, removing covered examples and searching for additional rules. FOIL's use of first-order logic makes the rules it learns more generally applicable than propositional rules.
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.
It proposes a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole. The mathematical models of these three concepts are developed to perform exploration, exploitation, and local search, respectively. MVO is compared with four well known algorithms: Grey Wolf Optimizer, Particle Swarm Optimization, Genetic Algorithm, and Gravitational Search Algorithm. The results prove that the proposed algorithm is able to provide very competitive results and outperforms the best algorithms in the literature on the majority of the test beds.
The document discusses several MAC protocols for ad hoc networks including MACA, MACAW, and PAMAS. MACA uses RTS and CTS packets to avoid collisions but does not provide ACK. MACAW is a revision of MACA that includes ACK. It significantly increases throughput but does not fully solve hidden and exposed terminal problems. PAMAS uses a separate signaling channel for RTS-CTS and a data channel. It allows nodes to power down transceivers when not transmitting to save energy.
This document contains information about an artificial intelligence course, including:
1) The course is taught by Dr. Amelia Ritahani Ismail in the Department of Computer Science at Kulliyyah of ICT.
2) The schedule for the semester is provided, outlining the topics to be covered each week, including assignments, quizzes, exams, and a group project.
3) An introduction to principles of artificial intelligence is given, defining AI, describing types of AI including traditional and computational intelligence approaches, and providing examples of applications.
Genetic algorithms and traditional algorithms differ in their definitions, usages, and complexity. Genetic algorithms are based on genetics and natural selection, and help find optimal solutions to difficult problems. They are more advanced than traditional algorithms which provide step-by-step procedures. Genetic algorithms are used in fields like machine learning and artificial intelligence, while traditional algorithms are used in programming and mathematics.
This document provides an overview of Chapter 14 on probabilistic reasoning and Bayesian networks from an artificial intelligence textbook. It introduces Bayesian networks as a way to represent knowledge over uncertain domains using directed graphs. Each node corresponds to a variable and arrows represent conditional dependencies between variables. The document explains how Bayesian networks can encode a joint probability distribution and represent conditional independence relationships. It also discusses techniques for efficiently representing conditional distributions in Bayesian networks, including noisy logical relationships and continuous variables. The chapter covers exact and approximate inference methods for Bayesian networks.
This document presents a presentation on fuzzy expert systems. It introduces expert systems and how they combine human expertise with computational capabilities. It then discusses the evolution of fuzzy expert systems to handle imprecision and uncertainty. The key components of a fuzzy expert system are described, including the knowledge base, inference engine, and user interface. Steps for constructing a fuzzy expert system are outlined, from knowledge representation to testing rules. Pros and cons as well as applications in various domains like agriculture, education, and medicine are also summarized.
The document discusses the AdaBoost classifier algorithm. AdaBoost is an algorithm that combines multiple weak classifiers to produce a strong classifier. It works by training weak classifiers on weighted versions of the training data and combining them through a weighted majority vote. The weights are updated at each iteration to focus on misclassified examples. The final strong classifier is a linear combination of the weak classifiers.
The document discusses various model-based clustering techniques for handling high-dimensional data, including expectation-maximization, conceptual clustering using COBWEB, self-organizing maps, subspace clustering with CLIQUE and PROCLUS, and frequent pattern-based clustering. It provides details on the methodology and assumptions of each technique.
Fuzzy inference systems use fuzzy logic to map inputs to outputs. There are two main types:
Mamdani systems use fuzzy outputs and are well-suited for problems involving human expert knowledge. Sugeno systems have faster computation using linear or constant outputs.
The fuzzy inference process involves fuzzifying inputs, applying fuzzy logic operators, and using if-then rules. Outputs are determined through implication, aggregation, and defuzzification. Mamdani systems find the centroid of fuzzy outputs while Sugeno uses weighted averages, making it more efficient.
The document summarizes the counterpropagation neural network algorithm. It consists of an input layer, a Kohonen hidden layer that clusters inputs, and a Grossberg output layer. The algorithm identifies the winning hidden neuron that is most activated by the input. The output is then calculated as the weight between the winning hidden neuron and the output neurons, providing a coarse approximation of the input-output mapping.
Machine learning is important for improving brittle early AI systems and reducing the effort required for knowledge acquisition. There are two main types of machine learning - supervised learning, where a system is provided examples and feedback to learn a task, and unsupervised learning, where patterns are identified without labeled examples. Popular supervised learning methods include neural networks, Bayesian classifiers, decision trees, and linear regression, which aim to learn functions or classify inputs. Bayesian learning estimates probabilities to classify inputs, while neural networks can perform non-linear regression through backpropagation.
This slide gives brief overview of supervised, unsupervised and reinforcement learning. Algorithms discussed are Naive Bayes, K nearest neighbour, SVM,decision tree, Markov model.
Difference between regression and classification. difference between supervised and reinforcement, iterative functioning of Markov model and machine learning applications.
The document discusses analytical learning methods like explanation-based learning. It explains that analytical learning uses prior knowledge and deductive reasoning to augment training examples, allowing it to generalize better than methods relying solely on data. Explanation-based learning analyzes examples according to prior knowledge to infer relevant features. The document provides examples of using explanation-based learning to learn chess concepts and safe stacking of objects. It also describes the PROLOG-EBG algorithm for explanation-based learning.
Medium Access Control :-
1.Distributed Operation
2.Synchronization
3.Hidden Terminals
4.Exposed terminals
5.Throughput
6.Access delay
7.Fairness
8.Real-time Traffic support
9.Resource reservation
10.Ability to measure resource availability
11.Capability for power control
Adaptive rate control
Use of directional antennas
Self-organizing networks can perform unsupervised clustering by mapping high-dimensional input patterns into a smaller number of clusters in output space through competitive learning. Fixed weight competitive networks like Maxnet, Mexican Hat net, and Hamming net use competitive learning with fixed weights. Maxnet uses winner-take-all competition to select the neuron whose weights best match the input. Mexican Hat net has both excitatory and inhibitory connections between neurons to enhance contrast. Hamming net determines which exemplar vector most closely matches the input using the Hamming distance measure.
The document discusses geo-cast routing protocols, which deliver data packets to nodes within a specified geographic region. It describes two categories of geo-cast protocols: data-transmission oriented protocols, which focus on transmitting information from source to geographic region, and routing creation oriented protocols, which aim to reduce flooding overhead while maintaining delivery accuracy. Specific protocols discussed include Location-Based Multicast, Geo-GRID, Geo-TORA, and mesh-based geo-cast routing. The document concludes by noting open issues like scalability, applications, addressing, and security for geo-cast routing over mobile ad hoc networks.
Boosting techniques like AdaBoost combine the predictions of many weak learner models to create a stronger joint model. AdaBoost uses stumps, or decision trees with one node and two leaves, as the weak learners. It adjusts the weights of samples to focus on incorrectly classified samples. Over many iterations, it boosts the weights of harder to classify samples to improve predictive performance compared to a single weak learner.
FOIL is an algorithm for inductive logic programming that learns sets of first-order rules from examples to perform tasks like predicting relationships between people. FOIL extends earlier rule learning algorithms to handle first-order logic representations using predicates, variables, and quantification. It searches for the most specific rules that cover positive training examples, removing covered examples and searching for additional rules. FOIL's use of first-order logic makes the rules it learns more generally applicable than propositional rules.
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.
It proposes a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole. The mathematical models of these three concepts are developed to perform exploration, exploitation, and local search, respectively. MVO is compared with four well known algorithms: Grey Wolf Optimizer, Particle Swarm Optimization, Genetic Algorithm, and Gravitational Search Algorithm. The results prove that the proposed algorithm is able to provide very competitive results and outperforms the best algorithms in the literature on the majority of the test beds.
The document discusses several MAC protocols for ad hoc networks including MACA, MACAW, and PAMAS. MACA uses RTS and CTS packets to avoid collisions but does not provide ACK. MACAW is a revision of MACA that includes ACK. It significantly increases throughput but does not fully solve hidden and exposed terminal problems. PAMAS uses a separate signaling channel for RTS-CTS and a data channel. It allows nodes to power down transceivers when not transmitting to save energy.
This document contains information about an artificial intelligence course, including:
1) The course is taught by Dr. Amelia Ritahani Ismail in the Department of Computer Science at Kulliyyah of ICT.
2) The schedule for the semester is provided, outlining the topics to be covered each week, including assignments, quizzes, exams, and a group project.
3) An introduction to principles of artificial intelligence is given, defining AI, describing types of AI including traditional and computational intelligence approaches, and providing examples of applications.
Genetic algorithms and traditional algorithms differ in their definitions, usages, and complexity. Genetic algorithms are based on genetics and natural selection, and help find optimal solutions to difficult problems. They are more advanced than traditional algorithms which provide step-by-step procedures. Genetic algorithms are used in fields like machine learning and artificial intelligence, while traditional algorithms are used in programming and mathematics.
This document provides an overview of Chapter 14 on probabilistic reasoning and Bayesian networks from an artificial intelligence textbook. It introduces Bayesian networks as a way to represent knowledge over uncertain domains using directed graphs. Each node corresponds to a variable and arrows represent conditional dependencies between variables. The document explains how Bayesian networks can encode a joint probability distribution and represent conditional independence relationships. It also discusses techniques for efficiently representing conditional distributions in Bayesian networks, including noisy logical relationships and continuous variables. The chapter covers exact and approximate inference methods for Bayesian networks.
This document presents a presentation on fuzzy expert systems. It introduces expert systems and how they combine human expertise with computational capabilities. It then discusses the evolution of fuzzy expert systems to handle imprecision and uncertainty. The key components of a fuzzy expert system are described, including the knowledge base, inference engine, and user interface. Steps for constructing a fuzzy expert system are outlined, from knowledge representation to testing rules. Pros and cons as well as applications in various domains like agriculture, education, and medicine are also summarized.
The document discusses the AdaBoost classifier algorithm. AdaBoost is an algorithm that combines multiple weak classifiers to produce a strong classifier. It works by training weak classifiers on weighted versions of the training data and combining them through a weighted majority vote. The weights are updated at each iteration to focus on misclassified examples. The final strong classifier is a linear combination of the weak classifiers.
The document discusses various model-based clustering techniques for handling high-dimensional data, including expectation-maximization, conceptual clustering using COBWEB, self-organizing maps, subspace clustering with CLIQUE and PROCLUS, and frequent pattern-based clustering. It provides details on the methodology and assumptions of each technique.
Fuzzy inference systems use fuzzy logic to map inputs to outputs. There are two main types:
Mamdani systems use fuzzy outputs and are well-suited for problems involving human expert knowledge. Sugeno systems have faster computation using linear or constant outputs.
The fuzzy inference process involves fuzzifying inputs, applying fuzzy logic operators, and using if-then rules. Outputs are determined through implication, aggregation, and defuzzification. Mamdani systems find the centroid of fuzzy outputs while Sugeno uses weighted averages, making it more efficient.
The document summarizes the counterpropagation neural network algorithm. It consists of an input layer, a Kohonen hidden layer that clusters inputs, and a Grossberg output layer. The algorithm identifies the winning hidden neuron that is most activated by the input. The output is then calculated as the weight between the winning hidden neuron and the output neurons, providing a coarse approximation of the input-output mapping.
Machine learning is important for improving brittle early AI systems and reducing the effort required for knowledge acquisition. There are two main types of machine learning - supervised learning, where a system is provided examples and feedback to learn a task, and unsupervised learning, where patterns are identified without labeled examples. Popular supervised learning methods include neural networks, Bayesian classifiers, decision trees, and linear regression, which aim to learn functions or classify inputs. Bayesian learning estimates probabilities to classify inputs, while neural networks can perform non-linear regression through backpropagation.
This slide gives brief overview of supervised, unsupervised and reinforcement learning. Algorithms discussed are Naive Bayes, K nearest neighbour, SVM,decision tree, Markov model.
Difference between regression and classification. difference between supervised and reinforcement, iterative functioning of Markov model and machine learning applications.
The document discusses machine learning and learning agents in three main points:
1. It defines machine learning and discusses different types of machine learning tasks like supervised, unsupervised, and reinforcement learning.
2. It explains the key differences between traditional machine learning approaches and learning agents, noting that learning is one of many goals for agents and must be integrated with other agent functions.
3. It discusses different challenges of integrating machine learning into intelligent agents, such as balancing learning with recall of existing knowledge and addressing time constraints on learning from the environment.
This document provides an overview of machine learning and neural networks. It begins with an introduction to machine learning concepts like learning, learning agents, and applications. It then covers different types of machine learning including supervised, unsupervised, and reinforcement learning. Specific algorithms like linear discriminant analysis, perceptrons, and neural networks are explained at a high level. Key concepts of neural networks like neurons, network structure, and functioning are summarized.
This document provides an overview of various machine learning algorithms. It discusses supervised learning algorithms like decision trees, naive Bayes, and support vector machines. Unsupervised learning algorithms covered include k-means clustering and principal component analysis. Semi-supervised, reinforcement, and ensemble learning are also summarized. Neural networks and instance-based learning are described. A wide range of applications of machine learning are listed and the document concludes with future opportunities for machine learning.
This document provides an overview of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. It then discusses decision tree learning and decision trees in more detail. Decision tree algorithms like ID3 and C4.5 are explained as popular inductive inference algorithms that use an information gain measure to select attributes at each step of growing the decision tree. The document also covers converting decision trees to rules and splitting information. Linear models and artificial neural networks are briefly introduced, with the backpropagation algorithm explained as the gradient descent learning rule used in multilayer feedforward neural networks.
Machine learning was discussed including definitions, types, and examples. The three main types are supervised, unsupervised, and reinforcement learning. Supervised learning uses labeled training data to predict target variables for new data. Unsupervised learning identifies patterns in unlabeled data through clustering and association analysis. Reinforcement learning involves an agent learning through rewards and penalties as it interacts with an environment. Examples of machine learning applications were also provided.
1. The document discusses several approaches to cognitive science including connectionism, neural networks, supervised and unsupervised learning, Hebbian learning, the delta rule, backpropagation, and responses to Descartes from Gelernter, Penrose, and Pinker.
2. Connectionism models mental phenomena using interconnected networks of simple units like neural networks. Learning involves adjusting connection weights between neurons.
3. Supervised learning uses input-output pairs to adjust weights to minimize error, while unsupervised learning only uses inputs to find patterns in the data.
The document provides an introduction to machine learning and neural networks. It defines machine learning as a field that allows computers to learn without being explicitly programmed. It also discusses different machine learning algorithms like supervised learning, unsupervised learning, and reinforcement learning. The document then describes neural networks and their biological inspiration from the human brain. It explains the basic structure and functioning of artificial neurons and neural networks. Finally, it discusses common neural network training techniques like backpropagation that are used to minimize errors and update weights in multi-layer neural networks.
This document provides an introduction to machine learning and neural networks. It defines machine learning as a field that allows computers to learn without being explicitly programmed. It also describes the main types of machine learning as supervised learning, unsupervised learning, and reinforcement learning. The document then discusses neural networks and their biological inspiration from the human brain. It provides examples of neural network applications and describes the basic structure and functioning of neural networks.
Machine Learning an Exploratory Tool: Key Conceptsachakracu
This was an Online Lecture Describing Key Concepts of Machine Learning Strategies inclusing Neural Networks
National Webinar On Education 4.0 “Ensuring Continuity in Learning and Innovation Through Digitization”
Organized By: Singhad Institute of Management, Pune in Association with Savitribai Phule Pune University
12th June 2020
The document discusses different approaches to artificial intelligence, including rule-based and learning-based systems. It describes rule-based systems as using if-then rules to reach conclusions, while learning-based systems can adapt existing knowledge through learning. Machine learning is discussed as a type of learning-based AI that allows systems to learn from data without being explicitly programmed. Deep learning is described as a subset of machine learning that uses neural networks with multiple layers to learn from examples in a way similar to the human brain.
In a world of data explosion, the rate of data generation and consumption is on the increasing side,
there comes the buzzword - Big Data.
Big Data is the concept of fast-moving, large-volume data in varying dimensions (sources) and
highly unpredicted sources.
The 4Vs of Big Data
● Volume - Scale of Data
● Velocity - Analysis of Streaming Data
● Variety - Different forms of Data
● Veracity - Uncertainty of Data
With increasing data availability, the new trend in the industry demands not just data collection but making an ample sense of acquired data - thereby, the concept of Data Analytics.
Taking it a step further to further make futuristic prediction and realistic inferences - the concept
of Machine Learning.
A blend of both gives a robust analysis of data for the past, now and the future.
There is a thin line between data analytics and Machine learning which becomes very obvious
when you dig deep.
In a world of data explosion, the rate of data generation and consumption is on the increasing side, there comes the buzzword - Big Data.
Big Data is the concept of fast-moving, large-volume data in varying dimensions (sources) and
highly unpredicted sources.
The 4Vs of Big Data
● Volume - Scale of Data
● Velocity - Analysis of Streaming Data
● Variety - Different forms of Data
● Veracity - Uncertainty of Data
With increasing data availability, the new trend in the industry demands not just data collection,
but making ample sense of acquired data - thereby, the concept of Data Analytics.
Taking it a step further to further make a futuristic prediction and realistic inferences - the concept
of Machine Learning.
A blend of both gives a robust analysis of data for the past, now and the future.
There is a thin line between data analytics and Machine learning which becomes very obvious
when you dig deep.
Chapter 6 - Learning data and analytics coursegideymichael
The document discusses machine learning and concept learning. It introduces concept learning as learning a function that maps examples into categories. An example of concept learning is classifying mushrooms as poisonous or not based on their attributes. The key aspects of concept learning covered are:
- Representing hypotheses as conjunctions of attributes and values
- Defining a general to specific ordering of hypotheses
- Searching the hypothesis space using an algorithm that starts with the most specific hypothesis and generalizes it when it fails to cover positive examples
The goal is to find the maximally specific hypothesis that is consistent with all training examples.
Machine learning and its applications were presented. Machine learning is defined as algorithms that improve performance on tasks through experience. There are supervised and unsupervised learning methods. Supervised learning uses labeled training data, while unsupervised learning finds patterns in unlabeled data. Deep learning uses neural networks with many layers to perform complex feature identification and processing. Deep learning has achieved state-of-the-art results in areas like image recognition, speech recognition, and autonomous vehicles.
The document provides an introduction to artificial neural networks and deep learning. It discusses the biological inspiration for artificial neural networks from the human brain. It then describes the basic components of artificial neurons and different network architectures like feedforward and recurrent networks. The document also introduces machine learning concepts like supervised and unsupervised learning. It explains how neural networks are trained and can learn from data. Finally, it provides examples of deep learning models and applications in various domains like science, manufacturing and finance.
The document provides an overview of machine learning concepts and applications in bioinformatics. It discusses key topics like supervised vs unsupervised learning, classification vs regression, linear vs non-linear models, and examples of machine learning algorithms like naive Bayes, neural networks, and support vector machines. Specific examples mentioned include using these algorithms for protein function prediction, gene finding, and predicting RNA binding sites in proteins.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Road construction is not as easy as it seems to be, it includes various steps and it starts with its designing and
structure including the traffic volume consideration. Then base layer is done by bulldozers and levelers and after
base surface coating has to be done. For giving road a smooth surface with flexibility, Asphalt concrete is used.
Asphalt requires an aggregate sub base material layer, and then a base layer to be put into first place. Asphalt road
construction is formulated to support the heavy traffic load and climatic conditions. It is 100% recyclable and
saving non renewable natural resources.
With the advancement of technology, Asphalt technology gives assurance about the good drainage system and with
skid resistance it can be used where safety is necessary such as outsidethe schools.
The largest use of Asphalt is for making asphalt concrete for road surfaces. It is widely used in airports around the
world due to the sturdiness and ability to be repaired quickly, it is widely used for runways dedicated to aircraft
landing and taking off. Asphalt is normally stored and transported at 150’C or 300’F temperature
Levelised Cost of Hydrogen (LCOH) Calculator ManualMassimo Talia
The aim of this manual is to explain the
methodology behind the Levelized Cost of
Hydrogen (LCOH) calculator. Moreover, this
manual also demonstrates how the calculator
can be used for estimating the expenses associated with hydrogen production in Europe
using low-temperature electrolysis considering different sources of electricity
Impartiality as per ISO /IEC 17025:2017 StandardMuhammadJazib15
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Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
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2. Introduction to Machine Learning
Prepared By : Narayan Dhamala
Machine learning is the study of computer system that learn from
data and experience.
Machine learning is subfield of artificial intelligence which gives
the computer ability to learn without being explicitly programmed.
The goal of machine learning is to build computer system that
can adapt and learn from their experience.
A computer program is said to be learn if it‟s performance P
improves over a task T with an experience E.
2
3. Concept of Learning
Prepared By : Narayan Dhamala
Learning is a way of updating the knowledge.
Learning is making useful changes in our mind.
Learning is constructing or modifying representations of what is
being experienced.
Learning denotes changes in the system that are adaptive in the
sense that they enable the system to do the same task (or tasks
drawn from the same population) more effectively the next time.
3
4. Types of Learning
Prepared By : Narayan Dhamala
The strategies for learning can be classified according to the amount of inference the system
has to perform on its training data. In increasing order we have
1. Rote learning – the new knowledge is implanted directly with no inference at all, e.g. simple
memorization of past events, or a knowledge engineer‟s direct programming of rules elicited
from a human expert into an expert system.
2. Supervised learning – the system is supplied with a set of training examples consisting of
inputs and corresponding outputs, and is required to discover the relation or mapping
between then, e.g. as a series of rules, or a neural network.
3. Unsupervised learning – the system is supplied with a set of training examples consisting
only of inputs and is required to discover for itself what appropriate outputs should be, e.g. a
Kohonen Network or Self Organizing Map.
4. Reinforcement learning- Is concerned with how intelligent agents ought to act in an
environment to maximize some notion of reward from sequence of actions.
4
7. Learning Framework
Prepared By : Narayan Dhamala
7
• There are four major components in a learning system:
Environment
Learning
Element
Performance
Element
Knowledge
Base
8. Learning Framework:
The Environment
Prepared By : Narayan Dhamala
• The environment refers the nature and quality of information given to the
learning element
• The nature of information depends on its level (the degree of generality wrt
the performance element)
– high level information is abstract, it deals with a broad class of problems
– low level information is detailed, it deals with a single problem.
• The quality of information involves
– noise free
– reliable
– ordered
8
9. Learning Framework:
Learning Elements
Prepared By : Narayan Dhamala
• Four learning situations
– Rote Learning
• environment provides information at the required level
– Learning by being told
• information is too abstract, the learning element must hypothesize missing data
– Learning by example
• information is too specific, the learning element must hypothesize more general rules
– Learning by analogy
• information provided is relevant only to an analogous task, the learning element must
discover the analogy
9
10. Learning Framework:
Learning Elements
Prepared By : Narayan Dhamala
• Four learning situations
– Rote Learning
• environment provides information at the required level
– Learning by being told
• information is too abstract, the learning element must hypothesize missing data
– Learning by example
• information is too specific, the learning element must hypothesize more general rules
– Learning by analogy
• information provided is relevant only to an analogous task, the learning element must
discover the analogy
10
11. Learning Framework:
The Knowledge Base
Prepared By : Narayan Dhamala
• Expressive
– the representation contains the relevant knowledge in an easy to get to
fashion
• Modifiable
– it must be easy to change the data in the knowledge base
• Extendibility
– the knowledge base must contain meta-knowledge (knowledge on how
the data base is structured) so the system can change its structure
11
12. Learning Framework:
The Performance Element
Prepared By : Narayan Dhamala
• Complexity
– for learning, the simplest task is classification based on a single rule while
the most complex task requires the application of multiple rules in
sequence
• Feedback
– the performance element must send information to the learning system to
be used to evaluate the overall performance
• Transparency
– the learning element should have access to all the internal actions of the
performance element
12
13. Statistical Based Learning: Naïve Bayes Model
Prepared By : Narayan Dhamala
• Statistical Learning is a set of tools for understanding
data. These tools broadly come under two classes:
supervised learning & unsupervised learning.
• Generally, supervised learning refers to predicting or
estimating an output based on one or more inputs.
• Unsupervised learning, on the other hand, provides a
relationship or finds a pattern within the given data
without a supervised output.
13
14. BAYESIAN METHODS
• Learning and classification (Supervised learning) method
based on probability theory.
• Baye‟s theorem plays a critical role in probabilistic
learning and classification.
• Uses prior probability of each category given no
information about an item.
• Categorization produces a posterior probability
distribution over the possible categories given a
description of an item.
P(A|B) =
15. BAYESIAN METHODS...
D =
AFTER TRAINING
Size<small, medium, large>
Color<red, blue, green>
Shape<circle, triangle, square>
Category<positive, negative>
19. Learning by genetic algorithm
Genetic algorithm is an evolutionary algorithm which is based
on the principle of natural selection and natural genetics.
Genetic algorithm plays an important role in search and
optimization problem.
The main purpose of genetic algorithm is to find the
individuals from the search space with the best genetic
materials.
The genetic algorithm process consists of following 4 steps:
1) Encoding (Representation)
2) Selection
3) Crossover &
4) Mutation
20.
21.
22.
23.
24.
25.
26. Learning by Neural Networks
Neural Network
A neural network( also called artificial neural network) is a computing system made up of a
number of simple, highly interconnected processing elements, which process information by
their dynamic state response to external inputs.
An artificial neural network is an information processing paradigm that is inspired by
biological nervous system.
It is composed of large number of highly interconnected processing elements called
neurons.
Each neuron in ANN receives a number of inputs.
An activation function is applied to these inputs which results the output value of the neuron.
27. Learning by Neural Networks
Biological neural network vs Artificial neural network
The term "Artificial Neural Network" is derived from Biological neural networks that
develop the structure of a human brain. Similar to the human brain that has neurons
interconnected to one another, artificial neural networks also have neurons that are
interconnected to one another in various layers of the networks. These neurons are known
as nodes.
Dendrites from Biological Neural Network represent inputs in Artificial Neural Networks, cell
nucleus represents Nodes, synapse represents Weights, and Axon represents Output.
28. Learning by Neural Networks
Biological neural network vs Artificial neural network
The Relationship between Biological neural network and artificial neural network is as
follows
33. Types of ANN
The different types of ANN are as follows
1) Feed Forward ANN
Feed forward neural network is the simplest form of neural networks where input data
travels in one direction only, passing through artificial neural nodes and exiting through
output nodes.
The feed forward neural network does not contain loop or cycle.
In feed forward neural network, the hidden layers may or may not be present but the input
and output layers are present there.
Based on this, they can be further classified as a single-layered or multi-layered feed-
forward neural network.
35. Types of ANN
Note:
• Single Layer Perceptron – This is the simplest feed forward neural network which does
not contain any hidden layer.
• Multi Layer Perceptron – A Multi Layer Perceptron has one or more hidden layers.
Advantages of Feed forward Neural Network
• Less complex, easy to design & maintain
• Fast and speedy [One-way propagation]
• Highly responsive to noisy data
Dis-advantages of Feed forward Neural Network
Cannot be used for deep learning [due to absence of dense layers and back propagation]
36. Types of ANN
2) Recurrent (Feed back)Neural Network
Recurrent neural network is a type of neural network in which the output from the previous
steps are feed as input to the current step.
The recurrent neural network contains loop or cycle.
The main and most important feature of RNN is Hidden state, which remembers some
information about a sequence.
37. Types of ANN
Advantages of Recurrent Neural Networks
• Model sequential data where each sample can be assumed to be dependent on historical
ones is one of the advantage.
• Used with convolution layers to extend the pixel effectiveness.
Disadvantages of Recurrent Neural Networks
• Training recurrent neural nets could be a difficult task
• Difficult to process long sequential data using ReLU(rectified linear ) as an activation
function.
38. Advantages and Dis-advantages of Neural Network
Advantages:
• A neural network can perform tasks in parallel,which a linear program cannot perform.
• When an element of the neural network fails, it can continue without any problem by their
parallel nature.
• A neural network does not need to be reprogrammed as it learns itself.
• It can be implemented in an easy way without any problem.
• As adaptive, intelligent systems, neural networks are robust and excel at solving complex
problems. Neural networks are efficient in their programming and the scientists agree that
the advantages of using ANNs outweigh the risks.
• It can be implemented in any application.
Disadvantages:
• The neural network requires training to operate.
• Requires high processing time for large neural networks.
• The architecture of a neural network is different from the architecture and history of
microprocessors so they have to be emulated.
39. Applications of ANN
Brain modeling:
Aid our understanding of how the brain works, how behavior emerges from the interaction of
networks of neurons, what needs to “get fixed” in brain damaged patients .
Real world applications :
Financial modeling – predicting the stock market
Time series prediction – climate, weather, seizures
Computer games – intelligent agents, chess, backgammon
Robotics – autonomous adaptable robots
Pattern recognition – speech recognition, seismic activity, sonar signals
Data analysis – data compression, data mining
40. Learning by Training ANN
Training a Neural Network means finding the appropriate Weights of the Neural
Connections.
Once a network has been structured for a particular application, that network is ready to be
trained. To start this process the initial weights are chosen randomly. Then, the training, or
learning, begins.
There are two approaches to training - supervised and unsupervised.
Supervised training involves a mechanism of providing the network with the desired output
either by manually "grading" the network's performance or by providing the desired outputs
with the inputs.
Unsupervised training is where the network has to make sense of the inputs without
outside help.
Supervised Training
In supervised training, both the inputs and the outputs are provided. The network then
processes the inputs and compares its resulting outputs against the desired outputs. Errors
are then propagated back through the system, causing the system to adjust the weights
which control the network. This process occurs over and over as the weights are
continually tweaked. The set of data which enables the training is called the "training set."
During the training of a network the same set of data is processed many times as the
connection weights are ever refined.
41. Learning by Training ANN
Unsupervised Training
The other type of training is called unsupervised training. In unsupervised training, the
network is provided with inputs but not with desired outputs. The system itself must then
decide what features it will use to group the input data. This is often referred to as self-
organization or adaption.
46. Perceptron Learning
Learning a perceptron means finding the right values for Weight. The hypothesis space of
a perceptron is the space of all weight vectors.
The perceptron learning algorithm can be stated as below.
1. Assign random values to the weight vector
2. Apply the weight update rule to every training example
3. Are all training examples correctly classified?
a. Yes. Quit
b. b. No. Go back to Step 2.
There are two popular weight update rules.
i) The perceptron rule, and
ii) Delta rule