PresentationOfMachineLearning
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This document provides an overview of machine learning. It defines machine learning as a computer program improving its performance on tasks through experience. Examples of successful machine learning applications include speech recognition, autonomous vehicles, astronomical classification, and backgammon playing. The document lists medical diagnosis, data mining, bioinformatics, speech/handwriting recognition, product categorization, and information retrieval as applications of machine learning. It describes machine learning's importance in handling tasks that cannot be well-defined, finding relationships in large data, and adapting to changing environments.
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PresentationOfMachineLearning
- 1. Presentation of Discrete Math Course code: CSE 131
- 2. oup Name: BACKSLASH N
- 3. Group Members: NAME: ID 1. Azizul Hakim 152-15-6033 2. Habibur Rahman 152-15-6040 3. Mehedi Hassan 152-15-5815 4. Nur-un Nabi Biplob 152-15-5963
- 4. 4 Machine Learning Overview of Machine Learning
- 5. 5 Machine Learning: A Definition Definition: 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.
- 6. 6 Examples of Successful Applications of Machine Learning • Learning to recognize spoken words (Lee, 1989; Waibel, 1989). • Learning to drive an autonomous vehicle (Pomerleau, 1989). • Learning to classify new astronomical structures (Fayyad et al., 1995). • Learning to play world-class backgammon (Tesauro 1992, 1995).
- 7. The Brain: How does it work? .
- 11. Decision Tree Induction: Decision Boundary
- 12. Decision Tree Induction: Decision Boundary
- 13. Decision Tree Induction: Decision Boundary
- 14. APPLICATIONS: • Medical diagnosis • Data mining • Bioinformatics • Speech and handwriting recognition • Product categorization • Information retrieval
- 16. What is Data Mining? • Intersection of computer science and statistics • Data mining is process of finding correlations or patterns among large databases. • Data mining software is analytical tools for analyzing data.
- 17. What is a 'Pattern'? It is the probability of distribution of similar data. Or in other words its just a relation between the variables. 1. The computer sorts the data based on the algorithm. 2. If there is some deep change in data then, the algorithm tries to find relation between them and adapts accordingly. • Machine learning and Data Mining:
- 18. Machine learning for Medical Diagnosis Me Medical Diagnosis by Machine Learning
- 19. • Medical diagnosis: It is a procedure to identify disorder in a person. • Machine learning for medical diagnosis: It means that the computer will identify the symptoms and tell what that particular person is diagnosed with.
- 20. Medical Imaging: •CCD and GDV are types of image devices which have found great applications in Machine Learning Systems. •Medical Imaging is taking photos of body parts (both internal and external) and analyzing them for a disorder.
- 23. SEARCH ENGINES
- 26. 26 Why is Machine Learning Important? • Some tasks cannot be defined well, except by examples (e.g., recognizing people). • Relationships and correlations can be hidden within large amounts of data. Machine Learning/Data Mining may be able to find these relationships. • Human designers often produce machines that do not work as well as desired in the environments in which they are used.
- 27. 27 • The amount of knowledge available about certain tasks might be too large for explicit encoding by humans (e.g., medical diagnostic). • Environments change over time. • New knowledge about tasks is constantly being discovered by humans. It may be difficult to continuously re-design systems “by hand”. Why is Machine Learning Important (Cont’d)?