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.
This document provides an agenda and overview for a deep learning course. The agenda includes an introduction to program and course learning outcomes, the syllabus, class management tools, and an introduction to week 1 of deep learning. The syllabus outlines 15 weekly topics on deep learning concepts and algorithms. Example student projects are provided showing applications of deep learning to areas like computer vision, natural language processing, and games. The introduction to week 1 discusses artificial intelligence, machine learning, and deep learning definitions and provides an overview of programming assignments and deep learning in action.
CS 3491 Artificial Intelligence and Machine Learning Unit I Problem SolvingBalamuruganV28
This document provides an overview of problem solving agents and search algorithms covered in the CS 3491 - AI & ML course. The key topics covered include:
- Problem solving agents perceive their environment through sensors and act upon it through actuators. Common search algorithms like uninformed and heuristic search strategies are used.
- The problem solving process involves goal formulation, problem formulation, searching for a solution, and then executing the solution. The environment is assumed to be observable, discrete, known, and deterministic.
- State space refers to all reachable states from the initial state. Search algorithms take a problem as input and output an action sequence solution. Path cost assigns a numeric cost to each path. Common uninformed and informed
This document provides an overview of the COMP3170 Artificial Intelligence and Machine Learning course at Hong Kong Baptist University. It includes information about the course lecturers and textbook, assessment details, a tentative schedule of topics and dates, and a brief description of the content to be covered each week.
The document discusses Open Source GIS in South Korea. It provides background on perceptions of Open Source software in Korea and how those perceptions have changed over time. It outlines government policies and funding that now support Open Source GIS, including projects to develop an Open Source GIS platform and increase the ecosystem around Open Source GIS. It also describes the activities of OSGeo Korean Chapter and KAOS-G (Korea Open source GIS forum), including their participation in international conferences, translations of user interfaces, and training initiatives.
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.
This document provides an agenda and overview for a deep learning course. The agenda includes an introduction to program and course learning outcomes, the syllabus, class management tools, and an introduction to week 1 of deep learning. The syllabus outlines 15 weekly topics on deep learning concepts and algorithms. Example student projects are provided showing applications of deep learning to areas like computer vision, natural language processing, and games. The introduction to week 1 discusses artificial intelligence, machine learning, and deep learning definitions and provides an overview of programming assignments and deep learning in action.
CS 3491 Artificial Intelligence and Machine Learning Unit I Problem SolvingBalamuruganV28
This document provides an overview of problem solving agents and search algorithms covered in the CS 3491 - AI & ML course. The key topics covered include:
- Problem solving agents perceive their environment through sensors and act upon it through actuators. Common search algorithms like uninformed and heuristic search strategies are used.
- The problem solving process involves goal formulation, problem formulation, searching for a solution, and then executing the solution. The environment is assumed to be observable, discrete, known, and deterministic.
- State space refers to all reachable states from the initial state. Search algorithms take a problem as input and output an action sequence solution. Path cost assigns a numeric cost to each path. Common uninformed and informed
This document provides an overview of the COMP3170 Artificial Intelligence and Machine Learning course at Hong Kong Baptist University. It includes information about the course lecturers and textbook, assessment details, a tentative schedule of topics and dates, and a brief description of the content to be covered each week.
The document discusses Open Source GIS in South Korea. It provides background on perceptions of Open Source software in Korea and how those perceptions have changed over time. It outlines government policies and funding that now support Open Source GIS, including projects to develop an Open Source GIS platform and increase the ecosystem around Open Source GIS. It also describes the activities of OSGeo Korean Chapter and KAOS-G (Korea Open source GIS forum), including their participation in international conferences, translations of user interfaces, and training initiatives.
This document summarizes the analysis of five mobile learning game authoring tools: ARIS, App Inventor 2, Pocket Code, Furet Factory, and mLearn4web. Each tool was assessed based on the technical features it provides and its usability. ARIS was found to have many technical features but low usability due to complex terminology. App Inventor 2 and Pocket Code both received high marks for features due to visual programming, though Pocket Code's mobile-only interface hurt usability. Furet Factory was intuitive to use but lacked technical features. mLearn4web was moderately featured but lacked guidance. Overall assessments identified gaps to inform the design of a new authoring tool tailored for
Mannu Gera's urban planning portfolio summarizes their educational background and projects completed. It includes a table of contents, philosophy stating their interest in how urban design impacts people's lives, personal profile, descriptions of 5 projects including thesis on low-income housing, and semester-by-semester learning experiences covering topics like transportation, housing, and disaster recovery planning. Drawings and maps created for projects on areas in Delhi are also referenced.
Sakshi Sharma is a senior software developer with experience developing machine learning models and providing solutions to projects involving benefit validation, computer vision, and HR operations. She has skills in Python, data structures, algorithms, web scraping, MySQL, MongoDB, and ServiceNow. Her education includes an IT degree from Gyan Ganga Institute of Technology and Sciences. She has completed projects involving an HR helpdesk, benefit validation, resume parsing, a movie recommender system, an obstacle avoiding car, a computer lab management app, an educational app, and a social media app for municipal complaints.
This document discusses shaping strategies for artificial intelligence (AI). It provides examples of AI research centers in Japan, Germany, and the US and their focus areas. It also summarizes sample projects from each center related to areas like computer vision, healthcare, manufacturing, and transportation. Finally, it outlines AIRC's approach in Japan of creating working groups to match AI technologies and data with needs and pilot real-world applications.
This is the lecture delivered at Jadavpur University for the engineering students. The lecture was organised by the JU Entrepreneurship Cell and Alumni Association, Singapore Chapter.
The document analyzes the forensic investigation of a third-party social media application on a mobile device. It presents a hypothetical scenario involving user activities on the Instagram app. The analysis finds that user data like profiles, activity history, and settings are stored in the device file system and backup files. Comparing to past research, the analysis uses a deeper approach on a specific app and follows a hypothetical scenario to determine how user data is stored.
The document summarizes key topics from an Artificial Intelligence session, including different types of AI agents and the concepts that will be covered in the next session. It describes Simple Reflex Agents that take actions based only on current percepts. Model-Based Reflex Agents maintain an internal state based on percept history. Goal-Based Agents choose actions to achieve goals by considering long sequences of actions. Utility-Based Agents act to maximize utility by considering alternatives. Learning Agents can adapt by learning from experiences through a learning element, critic, performance element, and problem generator. The next session will cover Problem Solving Agents.
Deep Learning Algorithm Using Virtual Environment Data For Self-Driving Carsushilkumar1236
The document presents a deep learning algorithm for a self-driving car that uses computer vision techniques. It discusses using cameras, sensors, and machine learning models to process image data for tasks like lane detection, road sign identification, obstacle detection and avoidance. The design uses a convolutional neural network trained on thousands of images to classify objects. Experimental results showed this approach can reliably perform key computer vision tasks necessary for autonomous driving.
This document discusses object-oriented programming (OOP) and structured programming paradigms. It defines programming paradigms as fundamental styles of computer programming that classify languages. OOP focuses on modeling real-world entities and their relationships using objects that encapsulate both data and methods. In contrast, structured programming emphasizes functions and procedures that operate sequentially on passive data. The document also notes that OOP enables dividing complex systems into manageable modules and that its use of objects requires more memory but provides increased security compared to structured programming.
This document provides an overview of the first session of an Artificial Intelligence and Machine Learning course. It introduces key concepts in AI like intelligent agents, problem solving by search, and uninformed and informed search strategies. It defines AI as the ability of computers to learn and think. The session covered problem solving approaches, agent types, and rational agents. The topics to be covered in the next session include the different types of agents.
Artificial Intelligence power point presentation documentDavid Raj Kanthi
This document provides a certificate for a seminar report on the topic of artificial intelligence. It was completed by a student in partial fulfillment of an M.C.A. degree program in 2016-2017. The document includes an acknowledgment, declaration, abstract, and index sections that provide information about the student, guide, and overall content covered in the seminar report on artificial intelligence.
Deciphering AI: Human Expertise in the Age of Evolving AILiming Zhu
1) The document discusses how human expertise remains important in the age of evolving AI, especially as AI systems transition from narrow, rule-based approaches to more general and autonomous capabilities like deep learning and generative AI.
2) It provides examples of how human expertise can guide different AI approaches, from feature engineering for machine learning to providing feedback to help validate or invalidate systems.
3) The document also covers challenges around the business use of advanced AI, including how to ensure systems are explainable, accountable, and developed responsibly according to principles like fairness, privacy and reliability.
Tutorial on User Profiling with Graph Neural Networks and Related Beyond-Acc...Erasmo Purificato
Slide of the Tutorial on "User Profiling with Graph Neural Networks and Related Beyond-Accuracy Perspectives" @ UMAP'23: 31st ACM Conference on User Modeling, Adaptation and Personalization (June 26, 2023 | Limassol, Cyprus)
This document provides an introduction to the concepts of artificial intelligence and knowledge representation that will be covered in the course. It begins with definitions of AI and discusses its goals of replicating human intelligence and problem-solving abilities. It then covers topics like the history of AI, applications of AI systems, and different types of intelligent agents. The document also introduces concepts related to knowledge representation, including knowledge bases, semantic networks, frames, and other techniques. It aims to give students an overview of the key areas that will be examined in more depth during the course.
Projection Multi Scale Hashing Keyword Search in Multidimensional DatasetsIRJET Journal
The document discusses a novel method called ProMiSH (Projection and Multi Scale Hashing) for keyword search in multi-dimensional datasets. ProMiSH uses random projection and hash-based index structures to achieve high scalability and speedup of more than four orders over state-of-the-art tree-based techniques. Empirical studies on real and synthetic datasets of sizes up to 10 million objects and 100 dimensions show ProMiSH scales linearly with dataset size, dimension, query size, and result size. The method groups objects embedded in a vector space that are tagged with keywords matching a given query.
This document outlines an internship project analyzing Uber data using R language. It includes:
- An introduction describing the project goal of analyzing Uber pickup data in New York City using ggplot2 visualization.
- An overview of the data analysis architecture and machine learning methods used by Uber.
- A description of the project phases including importing packages, reading data, creating visualizations of trips by hour, day, month, and location.
- Snapshots of the visualizations created, such as heatmaps of trips by month, day, and location.
- A conclusion stating the project helped gain understanding of data visualization, manipulation, and machine learning concepts.
SCCAI- A Student Career Counselling Artificial Intelligencevivatechijri
As education is growing day by day, the competition has prompted a need for the student to
understand more about the educational field. Many times the counselor isn’t available all the time and
sometimes due to the lack of proper knowledge about some educational field. Due to this, it creates an issue of
misconception of that field. This creates a problem for the student to decide a proper educational trajectory and
guidance is not always useful. The proposed paper will overcome all these problem using machine learning
algorithm. Various algorithms are being considered and amongst them the best suitable for our project are used
here. There are 3 major problems that come across our path and they are solved using Random forest, Linear
regression and Searching algorithm using Google API. At first Searching algorithm solves the problem of
location by segregating the college’s location vice, then Random Forest provides the list of colleges by using
stream and range of percentage and finally Linear Regression predicts the current cutoff using previous years’
data. Rather than this, the proposed system also provides information regarding all fields of education helping
students to understand and know about their field of interest better. The following idea is a total fresh idea with
no existing projects of similar kind. This project will help students guide them throughout.
Introduction to KAOS-G at FOSS4G Osaka 2013slhead1
The document introduces KAOS-G (Korean Alliance of Open Source GIS), a forum of 8 small software companies in South Korea that aims to promote open source GIS. KAOS-G works to develop open source GIS technologies, share knowledge and experiences, and help improve South Korea's open source GIS ecosystem. Its goals include developing an integrated open source GIS package, providing training, and hosting the FOSS4G International conference in 2015. The group has already conducted training sessions and supported the FOSS4G Korea 2013 conference. KAOS-G seeks to help small Korean companies grow through open source GIS and expand globally with technologies that comply with international standards.
CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete RecordAsst.prof M.Gokilavani
LIST OF EXPERIMENTS:
1. Implement simple vector addition in Tensor Flow.
2. Implement a regression model in Keras.
3. Implement a perception in TensorFlow/Keras Environment.
4. Implement a Feed Forward Network in TensorFlow/Keras.
5. Implement an image classifier using CNN in TensorFlow/Keras.
6. Improve the deep Learning model by fine tuning hyper parameters.
7. Implement a Transfer Learning concept in image classification.
8. Using a pre trained model on Keras for transfer learning.
9. Perform Sentimental Analysis using RNN.
10. Implement an LSTM based Auto encoding inTensorflow/Keras.
11. Image generation using GAN.
ADDITIONAL EXPERIMENTS
12. Train a deep Learning model to classify a given image using pre trained model.
13. Recommendation system from sales data using Deep Learning.
14. Implement Object detection using CNN.
15. Implement any simple Reinforcement Algorithm for an NLP problem.
This document summarizes the analysis of five mobile learning game authoring tools: ARIS, App Inventor 2, Pocket Code, Furet Factory, and mLearn4web. Each tool was assessed based on the technical features it provides and its usability. ARIS was found to have many technical features but low usability due to complex terminology. App Inventor 2 and Pocket Code both received high marks for features due to visual programming, though Pocket Code's mobile-only interface hurt usability. Furet Factory was intuitive to use but lacked technical features. mLearn4web was moderately featured but lacked guidance. Overall assessments identified gaps to inform the design of a new authoring tool tailored for
Mannu Gera's urban planning portfolio summarizes their educational background and projects completed. It includes a table of contents, philosophy stating their interest in how urban design impacts people's lives, personal profile, descriptions of 5 projects including thesis on low-income housing, and semester-by-semester learning experiences covering topics like transportation, housing, and disaster recovery planning. Drawings and maps created for projects on areas in Delhi are also referenced.
Sakshi Sharma is a senior software developer with experience developing machine learning models and providing solutions to projects involving benefit validation, computer vision, and HR operations. She has skills in Python, data structures, algorithms, web scraping, MySQL, MongoDB, and ServiceNow. Her education includes an IT degree from Gyan Ganga Institute of Technology and Sciences. She has completed projects involving an HR helpdesk, benefit validation, resume parsing, a movie recommender system, an obstacle avoiding car, a computer lab management app, an educational app, and a social media app for municipal complaints.
This document discusses shaping strategies for artificial intelligence (AI). It provides examples of AI research centers in Japan, Germany, and the US and their focus areas. It also summarizes sample projects from each center related to areas like computer vision, healthcare, manufacturing, and transportation. Finally, it outlines AIRC's approach in Japan of creating working groups to match AI technologies and data with needs and pilot real-world applications.
This is the lecture delivered at Jadavpur University for the engineering students. The lecture was organised by the JU Entrepreneurship Cell and Alumni Association, Singapore Chapter.
The document analyzes the forensic investigation of a third-party social media application on a mobile device. It presents a hypothetical scenario involving user activities on the Instagram app. The analysis finds that user data like profiles, activity history, and settings are stored in the device file system and backup files. Comparing to past research, the analysis uses a deeper approach on a specific app and follows a hypothetical scenario to determine how user data is stored.
The document summarizes key topics from an Artificial Intelligence session, including different types of AI agents and the concepts that will be covered in the next session. It describes Simple Reflex Agents that take actions based only on current percepts. Model-Based Reflex Agents maintain an internal state based on percept history. Goal-Based Agents choose actions to achieve goals by considering long sequences of actions. Utility-Based Agents act to maximize utility by considering alternatives. Learning Agents can adapt by learning from experiences through a learning element, critic, performance element, and problem generator. The next session will cover Problem Solving Agents.
Deep Learning Algorithm Using Virtual Environment Data For Self-Driving Carsushilkumar1236
The document presents a deep learning algorithm for a self-driving car that uses computer vision techniques. It discusses using cameras, sensors, and machine learning models to process image data for tasks like lane detection, road sign identification, obstacle detection and avoidance. The design uses a convolutional neural network trained on thousands of images to classify objects. Experimental results showed this approach can reliably perform key computer vision tasks necessary for autonomous driving.
This document discusses object-oriented programming (OOP) and structured programming paradigms. It defines programming paradigms as fundamental styles of computer programming that classify languages. OOP focuses on modeling real-world entities and their relationships using objects that encapsulate both data and methods. In contrast, structured programming emphasizes functions and procedures that operate sequentially on passive data. The document also notes that OOP enables dividing complex systems into manageable modules and that its use of objects requires more memory but provides increased security compared to structured programming.
This document provides an overview of the first session of an Artificial Intelligence and Machine Learning course. It introduces key concepts in AI like intelligent agents, problem solving by search, and uninformed and informed search strategies. It defines AI as the ability of computers to learn and think. The session covered problem solving approaches, agent types, and rational agents. The topics to be covered in the next session include the different types of agents.
Artificial Intelligence power point presentation documentDavid Raj Kanthi
This document provides a certificate for a seminar report on the topic of artificial intelligence. It was completed by a student in partial fulfillment of an M.C.A. degree program in 2016-2017. The document includes an acknowledgment, declaration, abstract, and index sections that provide information about the student, guide, and overall content covered in the seminar report on artificial intelligence.
Deciphering AI: Human Expertise in the Age of Evolving AILiming Zhu
1) The document discusses how human expertise remains important in the age of evolving AI, especially as AI systems transition from narrow, rule-based approaches to more general and autonomous capabilities like deep learning and generative AI.
2) It provides examples of how human expertise can guide different AI approaches, from feature engineering for machine learning to providing feedback to help validate or invalidate systems.
3) The document also covers challenges around the business use of advanced AI, including how to ensure systems are explainable, accountable, and developed responsibly according to principles like fairness, privacy and reliability.
Tutorial on User Profiling with Graph Neural Networks and Related Beyond-Acc...Erasmo Purificato
Slide of the Tutorial on "User Profiling with Graph Neural Networks and Related Beyond-Accuracy Perspectives" @ UMAP'23: 31st ACM Conference on User Modeling, Adaptation and Personalization (June 26, 2023 | Limassol, Cyprus)
This document provides an introduction to the concepts of artificial intelligence and knowledge representation that will be covered in the course. It begins with definitions of AI and discusses its goals of replicating human intelligence and problem-solving abilities. It then covers topics like the history of AI, applications of AI systems, and different types of intelligent agents. The document also introduces concepts related to knowledge representation, including knowledge bases, semantic networks, frames, and other techniques. It aims to give students an overview of the key areas that will be examined in more depth during the course.
Projection Multi Scale Hashing Keyword Search in Multidimensional DatasetsIRJET Journal
The document discusses a novel method called ProMiSH (Projection and Multi Scale Hashing) for keyword search in multi-dimensional datasets. ProMiSH uses random projection and hash-based index structures to achieve high scalability and speedup of more than four orders over state-of-the-art tree-based techniques. Empirical studies on real and synthetic datasets of sizes up to 10 million objects and 100 dimensions show ProMiSH scales linearly with dataset size, dimension, query size, and result size. The method groups objects embedded in a vector space that are tagged with keywords matching a given query.
This document outlines an internship project analyzing Uber data using R language. It includes:
- An introduction describing the project goal of analyzing Uber pickup data in New York City using ggplot2 visualization.
- An overview of the data analysis architecture and machine learning methods used by Uber.
- A description of the project phases including importing packages, reading data, creating visualizations of trips by hour, day, month, and location.
- Snapshots of the visualizations created, such as heatmaps of trips by month, day, and location.
- A conclusion stating the project helped gain understanding of data visualization, manipulation, and machine learning concepts.
SCCAI- A Student Career Counselling Artificial Intelligencevivatechijri
As education is growing day by day, the competition has prompted a need for the student to
understand more about the educational field. Many times the counselor isn’t available all the time and
sometimes due to the lack of proper knowledge about some educational field. Due to this, it creates an issue of
misconception of that field. This creates a problem for the student to decide a proper educational trajectory and
guidance is not always useful. The proposed paper will overcome all these problem using machine learning
algorithm. Various algorithms are being considered and amongst them the best suitable for our project are used
here. There are 3 major problems that come across our path and they are solved using Random forest, Linear
regression and Searching algorithm using Google API. At first Searching algorithm solves the problem of
location by segregating the college’s location vice, then Random Forest provides the list of colleges by using
stream and range of percentage and finally Linear Regression predicts the current cutoff using previous years’
data. Rather than this, the proposed system also provides information regarding all fields of education helping
students to understand and know about their field of interest better. The following idea is a total fresh idea with
no existing projects of similar kind. This project will help students guide them throughout.
Introduction to KAOS-G at FOSS4G Osaka 2013slhead1
The document introduces KAOS-G (Korean Alliance of Open Source GIS), a forum of 8 small software companies in South Korea that aims to promote open source GIS. KAOS-G works to develop open source GIS technologies, share knowledge and experiences, and help improve South Korea's open source GIS ecosystem. Its goals include developing an integrated open source GIS package, providing training, and hosting the FOSS4G International conference in 2015. The group has already conducted training sessions and supported the FOSS4G Korea 2013 conference. KAOS-G seeks to help small Korean companies grow through open source GIS and expand globally with technologies that comply with international standards.
Similar to AI3391 ARTIFICAL INTELLIGENCE Session 4 Structure of agent .pptx (20)
CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete RecordAsst.prof M.Gokilavani
LIST OF EXPERIMENTS:
1. Implement simple vector addition in Tensor Flow.
2. Implement a regression model in Keras.
3. Implement a perception in TensorFlow/Keras Environment.
4. Implement a Feed Forward Network in TensorFlow/Keras.
5. Implement an image classifier using CNN in TensorFlow/Keras.
6. Improve the deep Learning model by fine tuning hyper parameters.
7. Implement a Transfer Learning concept in image classification.
8. Using a pre trained model on Keras for transfer learning.
9. Perform Sentimental Analysis using RNN.
10. Implement an LSTM based Auto encoding inTensorflow/Keras.
11. Image generation using GAN.
ADDITIONAL EXPERIMENTS
12. Train a deep Learning model to classify a given image using pre trained model.
13. Recommendation system from sales data using Deep Learning.
14. Implement Object detection using CNN.
15. Implement any simple Reinforcement Algorithm for an NLP problem.
CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdfAsst.prof M.Gokilavani
UNIT I INTRODUCTION
Neural Networks-Application Scope of Neural Networks-Artificial Neural Network: An IntroductionEvolution of Neural Networks-Basic Models of Artificial Neural Network- Important Terminologies of
ANNs-Supervised Learning Network.
This document provides a detailed syllabus for an Information Security course. It includes 5 units: Introduction, Security Investigation, Security Analysis, Logical Design, and Physical Design. The Introduction unit covers the history of information security and computer security. It defines key concepts like confidentiality, integrity, availability, and the CIA triangle. It also discusses security models and the components of an information system. The other units will cover topics like risk management, access control, security standards, cryptography, and physical security controls.
This document provides a detailed syllabus for an Information Security course. It covers 5 units:
1) Introduction - Provides a history of information security and an overview of key concepts like the CIA triangle of Confidentiality, Integrity and Availability.
2) Security Investigation - Covers the need for security, threats, attacks, and legal/ethical issues.
3) Security Analysis - Focuses on risk management, access controls, and information flow.
4) Logical Design - Addresses security policies, standards, security architecture design and planning continuity.
5) Physical Design - Covers security technologies, intrusion detection systems, cryptography, access controls, physical security and personnel security
Tuple assignment allows multiple variables to be assigned values from an iterable like a list or tuple in a single statement. This is more concise than separate assignments and avoids using a temporary variable. For example, to swap the values of variables a and b, tuple assignment can be used: a, b = b, a. The left side must contain the same number of variables as there are elements on the right, and each value is assigned to the corresponding variable from left to right. Tuple assignment is useful for unpacking elements like splitting a string into parts.
This document discusses control flow, functions, and recursion in Python. It begins by defining boolean expressions and explaining different types of operators like arithmetic, relational, logical, and assignment operators. It then covers conditional execution using if, else, and elif statements. Loops like while and for are explained along with break, continue, and pass statements. Functions are described as being able to return values. Finally, recursion is defined as a function calling itself, either directly or indirectly.
The document discusses various concepts related to data, expressions, and statements in Python programming. It begins by defining an interpreter as a program that executes instructions in a programming language. It then discusses invoking the Python interpreter in both script and interactive modes. In interactive mode, the interpreter provides immediate feedback for each statement. The document also defines various Python concepts like values and variables, keywords, expressions, operators, data types, functions, and control flow. It provides examples to illustrate function definition and calls, math functions, and basic Python programs to swap variables, check leap years, and convert Celsius to Fahrenheit.
This document outlines the syllabus for the course GE3151 Problem Solving and Python Programming. It contains 5 units that cover topics such as algorithmic problem solving, Python data types and expressions, control flow and functions in Python, Python lists, tuples and dictionaries, and files and modules in Python. The objectives of the course are to teach students how to solve problems using Python conditionals and loops, define Python functions, use Python data structures, perform input/output with files, and more. Each unit is allocated a certain number of periods to be taught and includes example programs to illustrate the concepts covered.
This document outlines the syllabus for the course GE3151 Problem Solving and Python Programming. It includes 5 units that cover topics like computational thinking, Python data types, control flow, functions, lists, tuples, dictionaries, files and modules. The objectives of the course are to understand algorithmic problem solving, learn to solve problems using Python conditionals and loops, define functions and use data structures like lists and tuples. It also aims to teach input/output with files in Python. The document provides the number of periods (45) and textbooks recommended for the course.
This document outlines the objectives and units of study for the course GE3151 Problem Solving and Python Programming. The course aims to teach algorithmic problem solving using Python conditionals, loops, functions, and data structures like lists, tuples and dictionaries. Students will learn to do input/output with files in Python. The 5 units cover computational thinking and problem solving, Python data types and statements, control flow and functions, lists, tuples and dictionaries, and files, modules and packages. Key concepts covered include algorithms, conditionals, iteration, functions, strings, lists, files operations like reading, writing and closing files, and exception handling.
The document provides information about the course GE3151 Problem Solving and Python Programming. It includes the objectives of the course, which are to understand algorithmic problem solving and learn to solve problems using Python constructs like conditionals, loops, functions, and data structures. It also outlines the 5 units that will be covered in the course, which include computational thinking, Python basics, control flow and functions, lists/tuples/dictionaries, and files/modules. Example problems and programs are provided for different sorting algorithms, quadratic equations, and list operations.
This document outlines the objectives and units of study for the course GE3151 Problem Solving and Python Programming. The objectives include understanding algorithmic problem solving, learning to solve problems using Python conditionals and loops, defining functions, and using data structures like lists, tuples and dictionaries. The 5 units of study are: Computational Thinking and Problem Solving, Data Types Expressions and Statements, Control Flow Functions and Strings, Lists Tuples and Dictionaries, and Files Modules and Packages. Some example problems and programs are provided for each unit to illustrate the concepts covered.
This document outlines the objectives and content of the course GE3151 Problem Solving and Python Programming. The course is intended to teach students the basics of algorithmic problem solving using Python. It covers topics like computational thinking, Python data types, control flow, functions, strings, lists, tuples, dictionaries, files and modules. The course contains 5 units that will teach students how to define problems, develop algorithms, implement solutions in Python using conditionals, loops, functions and data structures, perform input/output with files and use modules and packages.
This document provides an overview of first-order logic for knowledge representation in artificial intelligence. It discusses the syntax and semantics of first-order logic, including predicates, quantifiers, and variables. It also describes the knowledge engineering process for developing a first-order logic knowledge base, including identifying the problem domain, encoding general domain knowledge as rules, and representing a specific problem instance. Queries can then be posed to the knowledge base to infer answers using logical reasoning techniques like forward chaining and backward chaining.
AI3391 Artificial intelligence Session 29 Forward and backward chaining.pdfAsst.prof M.Gokilavani
The document summarizes topics to be covered in an AI course session. It includes:
1. Logical reasoning, propositional logic, theorem proving, model checking, and agents based on propositional logic will be discussed. First-order logic, syntax, semantics, knowledge representation, inference, and chaining will also be covered.
2. Examples of first-order logic, resolution proofs, and knowledge representation are provided.
3. The next session will cover acting under uncertainty.
The document outlines topics to be covered in an Artificial Intelligence session, including logical reasoning, propositional logic, first-order logic, knowledge representation, inference, and resolution theorem proving. It provides examples of converting statements to clausal form, constructing a resolution proof graph through unification of complementary literals, and uses an example problem to demonstrate resolving "John likes peanuts" through contradiction. The next session will cover forward and backward chaining inference techniques.
AI3391 Artificial intelligence session 27 inference and unification.pptxAsst.prof M.Gokilavani
This document summarizes an AI class session covering logical reasoning topics like propositional logic, first-order logic, and unification. It introduces propositional versus first-order logic and describes their differences. Key concepts from unification like the unification algorithm, most general unifier, and examples of unifying expressions are provided. The next class topics will cover resolution in first-order logic.
This document summarizes a session on first-order logic presented by Assistant Professor M. Gokilavani. The session covered the basics of first-order logic, including its syntax and semantics. Key points included: first-order logic uses predicates, terms, quantifiers, and connectives to represent relationships between objects; its syntax includes atomic sentences formed from predicates and terms, and complex sentences formed by combining atomic sentences; the universal and existential quantifiers allow representing statements that apply to all or some objects. Examples were provided to demonstrate representing statements in first-order logic. The next session will cover rules of inference in first-order logic.
Blood finder application project report (1).pdfKamal Acharya
Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.
We have designed & manufacture the Lubi Valves LBF series type of Butterfly Valves for General Utility Water applications as well as for HVAC applications.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
This presentation is about Food Delivery Systems and how they are developed using the Software Development Life Cycle (SDLC) and other methods. It explains the steps involved in creating a food delivery app, from planning and designing to testing and launching. The slide also covers different tools and technologies used to make these systems work efficiently.
Impartiality as per ISO /IEC 17025:2017 StandardMuhammadJazib15
This document provides basic guidelines for imparitallity requirement of ISO 17025. It defines in detial how it is met and wiudhwdih jdhsjdhwudjwkdbjwkdddddddddddkkkkkkkkkkkkkkkkkkkkkkkwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwioiiiiiiiiiiiii uwwwwwwwwwwwwwwwwhe wiqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq gbbbbbbbbbbbbb owdjjjjjjjjjjjjjjjjjjjj widhi owqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq uwdhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhwqiiiiiiiiiiiiiiiiiiiiiiiiiiiiw0pooooojjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj whhhhhhhhhhh wheeeeeeee wihieiiiiii wihe
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Height and depth gauge linear metrology.pdfq30122000
Height gauges may also be used to measure the height of an object by using the underside of the scriber as the datum. The datum may be permanently fixed or the height gauge may have provision to adjust the scale, this is done by sliding the scale vertically along the body of the height gauge by turning a fine feed screw at the top of the gauge; then with the scriber set to the same level as the base, the scale can be matched to it. This adjustment allows different scribers or probes to be used, as well as adjusting for any errors in a damaged or resharpened probe.
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
Sri Guru Hargobind Ji - Bandi Chor Guru.pdfBalvir Singh
Sri Guru Hargobind Ji (19 June 1595 - 3 March 1644) is revered as the Sixth Nanak.
• On 25 May 1606 Guru Arjan nominated his son Sri Hargobind Ji as his successor. Shortly
afterwards, Guru Arjan was arrested, tortured and killed by order of the Mogul Emperor
Jahangir.
• Guru Hargobind's succession ceremony took place on 24 June 1606. He was barely
eleven years old when he became 6th Guru.
• As ordered by Guru Arjan Dev Ji, he put on two swords, one indicated his spiritual
authority (PIRI) and the other, his temporal authority (MIRI). He thus for the first time
initiated military tradition in the Sikh faith to resist religious persecution, protect
people’s freedom and independence to practice religion by choice. He transformed
Sikhs to be Saints and Soldier.
• He had a long tenure as Guru, lasting 37 years, 9 months and 3 days
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
AI3391 ARTIFICAL INTELLIGENCE Session 4 Structure of agent .pptx
1. AI3391 ARTIFICAL INTELLIGENCE
(II YEAR (III Sem))
Department of Artificial Intelligence and Data Science
Session 4
by
Asst.Prof.M.Gokilavani
NIET
11/14/2023 Department of AI & DS 1
2. TEXTBOOK:
• Artificial Intelligence A modern Approach, Third Edition, Stuart
Russell and Peter Norvig, Pearson Education.
REFERENCES:
• Artificial Intelligence, 3rd Edn, E. Rich and K.Knight (TMH).
• Artificial Intelligence, 3rd Edn, Patrick Henny Winston, Pearson
Education.
• Artificial Intelligence, Shivani Goel, Pearson Education.
• Artificial Intelligence and Expert Systems- Patterson, Pearson
Education.
11/14/2023 Department of AI & DS 2
3. Topics covered in session 4
11/14/2023 Department of AI & DS 3
Unit I: Intelligent Agent
• Introduction to AI
• Agents and Environments
• Concept of Rationality
• Nature of environment
• Structure of Agents
• Problem solving agents
• Search Algorithm
• Uniform search Algorithm
4. Structure of an AI Agent
• The task of AI is to design an agent program which implements the
agent function.
• The structure of an intelligent agent is a combination of architecture
and agent program.
• It can be viewed as:
11/14/2023 Department of AI & DS 4
5. Following are the main three terms involved in the structure of an AI
agent:
• Architecture: Architecture is machinery that an AI agent executes on.
• Agent Function: Agent function is used to map a percept to an action.
• Agent program: Agent program is an implementation of agent
function. An agent program executes on the physical architecture to
produce function f.
11/14/2023 Department of AI & DS 5
6. PEAS Representation
• PEAS is a type of model on which an AI agent works upon. When
we define an AI agent or rational agent, then we can group its
properties under PEAS representation model. It is made up of
four words:
• P: Performance measure
• E: Environment
• A: Actuators
• S: Sensors
• Here performance measure is the objective for the success of an
agent's behavior.
11/14/2023 Department of AI & DS 6
7. Example: PEAS for self-driving cars
Let's suppose a self-driving car then PEAS representation will be:
• Performance: Safety, time, legal drive, comfort
• Environment: Roads, other vehicles, road signs, pedestrian
• Actuators: Steering, accelerator, brake, signal, horn
• Sensors: Camera, GPS, speedometer, odometer, accelerometer, sonar.
11/14/2023 Department of AI & DS 7