003 ppt machine learning

17 de Jan de 2021

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003 ppt machine learning

  1. Professional Practices Presented To Sir Ikram Ul Haq Presented By Tayyab Nawaz 17581556-003 BS-IT-17-A
  2. Machine Learning
  3. TABLE OF CONTENT 1. Definition 2. What is machine learning 3. Traditional programming and machine learning 4. Why machine learning is important 5. Generalization 6. Algorithms 7. Other learning techniques 8. Examples 9. Applications 10. Few quotes 11. Question and answers
  4. Introduction A branch of artificial intelligence, concerns the construction and study of systems that can learn from data.
  5. Definition In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed". “The goal of machine learning is to build computer systems that can adapt and learn from their experience.” Tom Dietterich. Tom M. Mitchell provided a widely quoted, more formal 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. So, What Is Machine Learning? Automating automation Getting computers to program themselves Writing software is the bottleneck Let the data do the work instead!
  7. Why Machine Learning is 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.
  8. Generalization A core objective of a learner is to generalize from its experience. Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set.
  9. Algorithm types Machine learning algorithms can be organized based on the desired outcome of the algorithm or the type of input available during training the machine 1. Supervised learning algorithms are trained on labeled examples, i.e., input where the desired output is known. 2. Unsupervised learning algorithms operate on unlabeled examples, i.e., input where the desired output is unknown.
  10. Algorithm types 3. Semi-supervised learning combines both labeled and unlabeled examples to generate an appropriate function or classifier. 4. Reinforcement learning is concerned with how intelligent agents ought to act in an environment to maximize some notion of reward from sequence of actions Other algorithms are:  Learning to learn  Developmental learning  Transduction etc.
  11. Other Learning Techniques Artificial neural networks Inductive logic programming Support vector machines Bayesian networks Reinforcement learning Association Rule learning Clustering
  12. Applications of Machine Learning Object recognition Natural language processing Search engines Brain-machine interfaces Stock market analysis Classifying DNA sequences Speech and handwriting recognition Software engineering Adaptive websites Robot locomotion Computational advertising Computational finance Recommender systems
  13. Software suites containing a variety of machine learning algorithms Ayasdi Apache Mahout Gesture Recognition Toolkit IBM SPSS Modeler MATLAB, mlpy Oracle Data Mining Orange Python scikit-learn SAS Enterprise Miner  STATISTICA Data Miner, and Weka
  14. A Few Quotes  “A breakthrough in machine learning would be worth ten Microsoft's” (Bill Gates, Chairman, Microsoft)  “Machine learning is the next Internet” (Tony Tether, Director, DARPA)  Machine learning is the hot new thing” (John Hennessy, President, Stanford)  “Web rankings today are mostly a matter of machine learning” (Prabhakar Raghavan, Dir. Research, Yahoo)  “Machine learning is today’s discontinuity” (Jerry Yang, CEO, Yahoo)
  15. Conclusion Machines should be able to do all the things what we can do & machine learning will play a big role in achieving this goal. References:- 1. Wikipedia 2. Machine learning summary - Greg Grudic CSCI-4830 3. CSE 546 Data Mining Machine Learning 4. SlideShare