Aravali College of Engineering and Management, Faridabad

1. Aravali College of Engineering and Management, Faridabad Department of Computer Science & Engineering(July – Dec 2020)

2. Learning AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

3. What is Learning? • “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.” AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

4. Learning Process AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

5. Learning system in Machine Learning AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

6. Machine Learning • “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) AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

7. Understanding Machine Learning from daily life applications AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

8. Machine Learning Machine learning is a subfield ofcomputer 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 programinstructions AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

9. Understanding the Concept of Machine Learning

10. Example: Spam Mail Detection • “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 AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

11. Applications of Machine Learning Facial recognition AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

12. Applications of Machine Learning Self-customizing programs (Netflix, Amazon, etc.) AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

13. Why Machine Learning? • 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 AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

14. How Machine Learning Different from Artificial Intelligence AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

15. Types Of Machine Learning 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 aface. 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 orhuman. AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

16. Types of Machine Learning REINFORCEMENTLEARNING: Learn how to behave successfully to achieve a goal while interacting with an external environment .(Learn viaExperiences!) AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

17. Supervised learning is themachine learning task of inferring a function from labeled training data. Thetraining dataconsist 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. AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

18. Supervised Learning AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

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20. Regression means to predict the output value using trainingdata. • Classification means to group the output into aclass. • e.g. we use regression to predict the house price from training data and use classification to predict theGender. 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. 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. AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

24. Supervised and Unsupervised Learning AASTHA BUDHIRAJA, DEPT. OF CSE, ACEM FARIDABAD

25. Aravali College of Engineering And Management Jasana, Tigoan Road, Neharpar, Faridabad, Delhi NCR Toll Free Number : 91- 8527538785 Website : www.acem.edu.in