Artificial intelligence
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This document discusses artificial intelligence (AI) and its goals of creating expert systems and implementing human intelligence in machines. It contributes to various fields including computer science, biology, philosophy, psychology, neuroscience, and math. The document also discusses types of intelligence, research areas in AI, types of learning including auditory, episodic, motor, observational, relational, spatial, perceptual, stimulus-response, and learning in AI including supervised, unsupervised, and reinforcement learning. It provides examples of supervised learning algorithms and unsupervised learning algorithms. Finally, it discusses artificial neural networks, including neurons, neural networks, types of neural networks such as feedforward and feedback networks, and applications of AI such as natural language processing.
Artificial intelligence
- 3. GOAL OF AITO CREATE EXPERT SYSTEMS TO IMPLEMENT HUMAN INTELLIGENCE IN MACHINES
- 4. CONTRIBUTE TO AI COMPUTER SCIENCE BIOLOGY PHILOSOPH Y PSYCHOLOG Y NEUROSCIEN CE MATHS
- 5. WHAT IS AN INTELLIGENCE ? LINGUISTIC MUSICAL SPATIAL LOGICAL AND MATHEMATICAL INTELLIGENCE INTELLIGENC E BODILY- KINESTHETIC INTRA PERSONAL INTER PERSONAL
- 6. RESEARCH AREAS OF ARTIFICIAL INTELLIGENCE ARTIFICI AL INTELLIG ENCE FUZZY LOGIC NATURAL LANGUAGE PROCESSIN G NEURAL NETWORK ROBOTICS EXPERT SYSTEMS
- 7. TYPES OF LEARNIING AUDIOTRY LEARNING :
- 12. SPATIAL LEARNING
- 18. SUPERVISED LEARNING ? Supervised Learning Algorithms: ? All classification and regression algorithms come under supervised learning. ? Logistic Regression ? Decision trees ? Support vector machine (SVM) ? k-Nearest Neighbors ? Naive Bayes ? Random forest ? Linear regression ? polynomial regression ? SVM for regression
- 21. UNSUPERVISED ? Unsupervised learning algorithms: ? All clustering algorithms come under unsupervised learning algorithms. ? K ¨C means clustering ? Hierarchical clustering ? Hidden Markov models
- 22. REINFORCEMENT LEARNING ? This type of Machine Learning algorithms allows software agents and machines to automatically determine the ideal behaviour within a specific context, to maximise its performance.
- 23. ANN(ARITIFICIAL NEURAL NETWORKS ? HUMAN BRAIN
- 24. NEURON
- 25. NEURAL NETWORKS ? An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. ? The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. ? An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons
- 27. TYPES OF NEURAL NETWORKS ? FeedForward ANN ? The information flow is unidirectional. A unit sends information to other unit from which it does not receive any information. There are no feedback loops. They are used in pattern generation/recognition/classification. They have fixed inputs and outputs.
- 29. FEEDBACK ANN ? feedback loops are allowed. They are used in content addressable memories
- 30. APPLICATION OF AI ? NATUARL LANGUAGE PROCESSING
Editor's Notes
- According to the father of Artificial Intelligence, John McCarthy, it is?¡°The science and engineering of making intelligent machines, especially intelligent computer programs¡±. Artificial Intelligence is a way of?making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think. AI is accomplished by studying how human brain thinks, and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems.
- The ability of a system to calculate, reason, perceive relationships and analogies, learn from experience, store and retrieve information from memory, solve problems, comprehend complex ideas, use natural language fluently, classify, generalize, and adapt new situations
- If you learn the thing before from training data and then applying that knowledge to the test data(for new fruit), This type of learning is called as?Supervised Learning.
- If you learn the thing before from training data and then applying that knowledge to the test data(for new fruit), This type of learning is called as?Supervised Learning.
- Learning from the unlabeled data to differentiating the given input data is called unsupervised data
- EVERYONE HAS WEIGHTS AND CLASSIFICATIONS