Acem machine learning
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This document discusses machine learning and different types of machine learning algorithms. It provides definitions of machine learning from Arthur Samuel and Tom Mitchell. It explains that machine learning allows computers to learn without being explicitly programmed. It distinguishes between supervised learning, unsupervised learning, and reinforcement learning. Supervised learning uses labeled training data to infer a function, while unsupervised learning identifies patterns in unlabeled data. The document gives examples of applying supervised learning to tasks like image classification and fraud detection.
Acem machine learning
- 1. Department of Computer Science & Engineering (July Dec 2020) 8/28/20201
- 2. 2 AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD
- 3. Learning denotes changes in a system that ... enable a system to do the same task more efficiently the next time. Learning is constructing or modifying representations of what is being experienced. Learning is making useful changes in our minds. Machine learning refers to a system capable of the autonomous acquisition and integration of knowledge. 3 AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD
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- 6. Field of study that gives computers the ability to learn without being explicitly programmed Arthur Samuel (1959) A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E Tom M. Mitchell (1998) 6 AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD
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- 8. Machine learning is a subfield of computer science that explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data- driven predictions or decisions, rather than following strictly static program instructions 8 AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD
- 10. A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E In our project, T: classify emails as spam or not spam E: watch the user label emails as spam or not spam 10 AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD
- 11. Facial recognition 11 AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD
- 12. Self-customizing programs (Netflix, Amazon, etc.) 12 AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD
- 13. No human experts industrial/manufacturing control mass spectrometer analysis, drug design, astronomic discovery Black-box human expertise face/handwriting/speech recognition driving a car, flying a plane Rapidly changing phenomena credit scoring, financial modeling diagnosis, fraud detection Need for customization/personalization personalized news reader movie/book recommendation 13 AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD
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- 15. Supervised learning : Learn by examples as to what a face is in terms of structure, color, etc so that after several iterations it learns to define a face. Unsupervised learning : since there is no desired output in this case that is provided therefore categorization is done so that the algorithm differentiates correctly between the face of a horse, cat or human. 15 AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD
- 16. REINFORCEMENT LEARNING: Learn how to behave successfully to achieve a goal while interacting with an external environment .(Learn via Experiences!) 16 AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD
- 17. Supervised learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object and a desired output value. A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. 17 AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD
- 18. Supervised Learning 18 AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD
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- 20. Regression means to predict the output value using training data. Classification means to group the output into a class. e.g. we use regression to predict the house price from training data and use classification to predict the Gender. 20 AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD
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- 22. Applications for supervised Learning Risk assessment - Supervised learning is used to assess the risk in financial services or insurance domains in order to minimize the risk portfolio of the companies. Image classification - Image classification is one of the key use cases of demonstrating supervised machine learning. For example, Facebook can recognize your friend in a picture from an album of tagged photos. Fraud detection - To identify whether the transactions made by the user are authentic or not. Visual recognition - The ability of a machine learning model to identify objects, places, people, actions and images. 22 AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD
- 23. Unsupervised Machine Learning In Unsupervised Learning, the machine uses unlabeled data and learns on itself without any supervision. The machine tries to find a pattern in the unlabeled data and gives a response. 23 AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD
- 24. Supervised and Unsupervised Learning 24 AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD
- 25. 8/28/202025 Aravali College of Engineering And Management Jasana, Tigoan Road, Neharpar, Faridabad, Delhi NCR Toll Free Number : 91- 8527538785 Website : www.acem.edu.in