13: Practical Artificial Intelligence & Machine Learning (Arturo Servin)
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This document provides an overview of artificial intelligence and machine learning concepts. It discusses different types of machine learning including supervised learning, unsupervised learning, and reinforcement learning. It also describes various machine learning algorithms like decision trees, Bayesian classifiers, artificial neural networks, and how they can be applied to problems like classification, clustering, and prediction. Finally, it lists several resources for learning more about artificial intelligence and machine learning through books, online courses, code libraries, and datasets.
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Linkedin :- http://in.linkedin.com/in/aniruddhtiwari
AI: Learning in AI
Neural networks can be used for machine learning tasks like classification. They consist of interconnected nodes that update their weight values during a training process using examples. Neural networks have been applied successfully to tasks like handwritten character recognition, autonomous vehicle control by observing human drivers, and text-to-speech pronunciation generation. Their architecture is inspired by the human brain but neural networks are trained using computational methods while the brain uses biological processes.
Linear Programming
This document discusses linear programming techniques for managerial decision making. Linear programming can determine the optimal allocation of scarce resources among competing demands. It consists of linear objectives and constraints where variables have a proportionate relationship. Essential elements of a linear programming model include limited resources, objectives to maximize or minimize, linear relationships between variables, homogeneity of products/resources, and divisibility of resources/products. The linear programming problem is formulated by defining variables and constraints, with the objective of optimizing a linear function subject to the constraints. It is then solved using graphical or simplex methods through an iterative process to find the optimal solution.
Artificial Intelligence Presentation
Artificial intelligence (AI) is the study and design of intelligent agents, with no single goal. It aims to put human-level intelligence into machines. The document traces the history of AI from its origins in 1941 to modern applications in areas like military, science, business, and entertainment. It discusses early developments like the Dartmouth conference that defined the field, and the creation of languages like Lisp and Prolog. Future developments may lead to more sophisticated AI in video games, self-governing robot societies, and abilities that surpass humans in games like chess, but this also raises ethical questions about controlling advanced AI.
Artificial Intelligence
The document discusses artificial intelligence, including its history, applications, and languages. It provides an overview of AI, noting that it aims to recreate human intelligence through machine learning and problem solving. The document then covers key topics like the philosophy of AI, limits on machine intelligence, and comparisons between human and artificial brains. It also gives brief histories of AI and machine learning. The document concludes by discussing popular AI programming languages like Lisp and Prolog, as well as various applications of AI technologies.
Machine Learning for Dummies
This document provides an introduction to machine learning, including:
- It discusses how the human brain learns to classify images and how machine learning systems are programmed to perform similar tasks.
- It provides an example of image classification using machine learning and discusses how machines are trained on sample data and then used to classify new queries.
- It outlines some common applications of machine learning in areas like banking, biomedicine, and computer/internet applications. It also discusses popular machine learning algorithms like Bayes networks, artificial neural networks, PCA, SVM classification, and K-means clustering.
Introduction to Machine Learning
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
Artificial inteligence
This document provides an introduction to artificial intelligence (AI). It defines AI as a branch of computer science dealing with symbolic and non-algorithmic problem solving. The document discusses the evolution of AI from early programs in the 1950s to current applications in areas like expert systems, natural language processing, computer vision, robotics, and automatic programming. It also notes both potential positive futures where intelligent robots assist humans as well as potential negative outcomes if robots are used for anti-social purposes. The conclusion is that AI has increased understanding of intelligence while also revealing its complexity.
Introduction to Machine Learning
Here are the key calculations:
1) Probability that persons p and q will be at the same hotel on a given day d is 1/100 × 1/100 × 10-5 = 10-9, since there are 100 hotels and each person stays in a hotel with probability 10-5 on any given day.
2) Probability that p and q will be at the same hotel on given days d1 and d2 is (10-9) × (10-9) = 10-18, since the events are independent.
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Internship - Python - AI ML.pptx
This document provides information about an internship in artificial intelligence using Python. It includes abbreviations commonly used in AI and machine learning and compares human organs to AI tools. It also discusses basics of AI, concepts in AI like machine learning and neural networks, qualities of humans and AI, important software for AI like Anaconda and TensorFlow, and types of machine learning algorithms. The document provides an overview of the topics that will be covered in the internship.
Machine Learning
This document introduces machine learning algorithms. It discusses supervised and unsupervised learning problems and strategies. It provides examples of machine learning applications including neural networks for handwritten digit recognition, evolutionary algorithms for nozzle design, and Bayesian networks for gene expression analysis.
Machine Learning
This document introduces machine learning algorithms. It discusses supervised and unsupervised learning problems and strategies. It provides examples of machine learning applications including neural networks for handwritten digit recognition, evolutionary algorithms for nozzle design, and Bayesian networks for gene expression analysis.
B4UConference_machine learning_deeplearning
Agenda
Understanding Machine Learning
Machine Learning Algorithms & Application
Demo Machine Learning
Why Deep Learning?
Deep Learning Algorithms & Application
Demo Deep Learning
Start machine learning in 5 simple steps
Simple steps to get started with machine learning.
The use case uses python programming. Target audience is expected to have a very basic python knowledge.
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Software continues to revolutionize the world, impacting nearly every aspect of our work, family, and personal life. Artificial intelligence (AI) and machine learning (ML) are playing key roles in this revolution through improvements in search results, recommendations, forecasts, and other predictions. AI and ML technologies are being used in platforms for digital assistants, home entertainment, medical diagnosis, customer support, and autonomous vehicles. Testing practitioners are recognizing the potential for advances in AI and ML to be leveraged for automated testing—an area that still requires significant manual effort. Tariq King and Jason Arbon introduce you to the world of AI for software testing. Learn the fundamentals behind autonomous and intelligent agents, ML approaches including Bayesian networks, decision tree learning, neural networks, and reinforcement learning. Discover how to apply these techniques to common testing tasks such as identifying testable features, generating test flows, and detecting erroneous states.
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This document discusses machine learning concepts including algorithms, data inputs/outputs, runtimes, and trends in academia vs industry. It notes that while academia focuses on algorithm complexity, industry prioritizes data-driven approaches using large datasets. Ensemble methods combining many simple models generally perform better than single complex models. Specific ML techniques discussed include word segmentation using n-gram probabilities, perceptrons for classification, SVD for recommendations and clustering, and crowdsourcing ensembles. The key lessons are that simple models with large data outperform complex models with less data, and that embracing many small independent models through ensembles is effective.
Meetup 29042015
This document provides an overview of machine learning, including:
1) It defines machine learning as teaching computational machines to solve problems by giving examples to automatically infer rules for associating inputs and outputs.
2) It discusses different machine learning algorithms like linear classifiers, support vector machines, ensemble methods, and deep learning.
3) It emphasizes the need for scalable deployment of machine learning models to handle large and streaming data, covering approaches like distributed and parallel processing using MapReduce and cloud services.
original
This document summarizes a machine learning lecture given by William H. Hsu. The 3-sentence summary is:
The lecture outlined the course information, including format, exams, homework and grading. It provided an overview of machine learning topics to be covered, such as learning algorithms, models and methodologies. Examples of applications of machine learning were also discussed, such as database mining, reasoning, and acting.
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1. Machine learning is the use and development of computer systems that are able to learn and adapt without explicit instructions by using algorithms and statistical models to analyze patterns in data.
2. The document provides examples of machine learning applications like facial recognition, voice recognition in healthcare, weather forecasting, and more. It also discusses the process of machine learning and popular machine learning algorithms.
3. The document demonstrates machine learning using a decision tree algorithm on music purchase data to predict whether a customer is male or female based on attributes like age and number of songs purchased. It imports relevant Python libraries and splits the data into training and test sets to evaluate the model's performance.
Ml programming with python
Machine Learning with Python discusses machine learning concepts and the Python tools used for machine learning. It introduces machine learning terminology and different types of learning. It describes the Pandas, Matplotlib and scikit-learn frameworks for data analysis and machine learning in Python. Examples show simple programs for supervised learning using linear regression and unsupervised learning using K-means clustering.
ML_ Unit_1_PART_A
The document provides an overview of machine learning concepts including:
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- Describing examples like speech recognition, medical diagnosis, and game playing
- Outlining the components of designing a machine learning system like choosing the target function, representation, and learning algorithm
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Analysis using r
This is all about the basic steps to be considered in the data analysis, which will be helpful for the beginners.
Machine Learning Contents.pptx
1. The document discusses machine learning types including supervised learning, unsupervised learning, and reinforcement learning. It provides examples of applications like spam filtering, recommendations, and fraud detection.
2. Key challenges in machine learning are discussed such as poor quality data, lack of training data, and imperfections when data grows.
3. The difference between data science and machine learning is explained - data science is a broader field that includes extracting insights from data using tools and models, while machine learning focuses specifically on making predictions using algorithms.
AutoML for Data Science Productivity and Toward Better Digital Decisions
AutoML is an approach to automating machine learning that aims to:
- Generate new machine learning pipelines
- Find good model pipelines by capturing and representing knowledge to "expertly" choose pipelines
- Use reinforcement learning with human input in real-time to guide iteration between pipelines to match how experts iterate
The key advantages of this AutoML approach are that it more closely mirrors how experts work by incorporating reasoning, knowledge representation, and human feedback into the process. This allows the algorithm to provide interpretable results and focus future trials based on learned performance, targeting faster results than traditional AutoML methods.
Machine Learning 1 - Introduction
The document discusses machine learning and provides examples of its applications. It introduces concepts such as learning from experience to improve performance, constructing learning algorithms, and representing the target function. Examples discussed include using patient data to predict high-risk pregnancies, using financial data to analyze credit risk, and learning to play checkers by representing the value of board positions and updating weights. Key questions in machine learning design are also summarized.
Artificial intelligence and IoT
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Machine learning and deep learning techniques are present in Java through various libraries. Deep learning allows neural networks to learn from vast amounts of data through multilayer architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The talk discussed several popular Java libraries that support both traditional machine learning algorithms and deep learning models, including DL4J, TensorFlow, Keras, and H2O. It provided examples of training deep learning models on MNIST and CIFAR10 datasets in DL4J and compared performance between DL4J and TensorFlow.
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