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Machine Learning techniques

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Machine Learning techniques

  1. 1. By -Jigar Patel M.Sc. Bioinformatics
  2. 2. What is Artificial intelligence? Is AI different then machine learning techniques? Basic Questions ……. Types of Machine Learning ?
  3. 3. Raw data • The empirical data is obtained from sensors and various other sources Scaling • The data is Scaled for the peak performance of the Algorithm Algorithms • Processing of the data using various algorithms to extract the underlying features(Training of the data) Output • The final Meaningful data (output) is obtained
  4. 4. 1. Machine learning, it’s a branch of Artificial intelligence, which is basically concerned with the development of algorithms that take as input empirical data, such as that from sensors or databases. 2. The data obtained is then processed by the algorithm is designed to identify complex relationships thought to be features of the underlying mechanism that generated the data, and employ these identified patterns to make predictions based on new data. 3. One fundamental difficulty is that the set of all possible behaviors given all possible inputs is (in most cases of practical interest) too large to be included in the set of observed examples. Hence the learner must generalize from the given examples in order to produce a useful output from new data inputs.
  5. 5. It is the process in which an algorithm can perform accurately on a new data (raw data) after the training of the data The training data used is generally of unknown probability distribution or a bit of noisy data so the learner has to extract something informative from the data which will lead to useful predictions
  6. 6. Supervised Learning Unsupervised Learning Reinforcement Learning Semi-supervised learning Transduction
  7. 7.  Supervised learning is the machine learning task of concluding a function from labeled training data  This inferred function should predict the correct output value for any valid input object.  The training data contains training examples or training values  In supervised learning the training data consist of the input object and the Output object(Supervisory signal ).  A supervised learning algorithm analyzes the training data and produces an inferred function, which is called a classifier
  8. 8. 1. Nature of Training data 2. Build a Training set. 3. Feature selection from the Training set 4. Determine the Structure of learned Function Eg. SOM, SVM, ANN 5. Test the accuracy of the function.
  9. 9.  Unsupervised Learning is used when the data is unlabeled or we have to find out the hidden structure.  Since the examples given to the learner are unlabeled, there is no error to evaluate a potential solution.  This is the distinguishing feature that separates from supervised learning and reinforced learning  Eg. Clustering ,PCA,SOM
  10. 10.  This method works on reinforcement from the outside. The connections among the neurons in the hidden layer are randomly arranged, then reshuffled as the network is told how close it is to solving the problem.  Reinforcement learning is also called supervised learning, because it requires a teacher. The teacher may be a training set of data or an observer who grades the performance of the network results.  Both unsupervised and reinforcement suffer from relative slowness and inefficiency relying on a random shuffling to find the proper connection weights.  Eg. Genetic Algorithm
  11. 11. ANN 1. Artificial neural networks is a type of learning algorithm that use Neural network (NN) which are inspired from biological neurons . 2. It consists of nodes and layers Input, hidden layer, output layer 3. Each layers are connected by nodes and specific weights values used for calculations.
  12. 12. Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. Its consist of many steps and uses support vectors ,Hyper planes and feature space
  13. 13. 1. Acquisition /Sensing of data(raw sequences ) 2. Pre-Processing(remove redundancy) 3. Feature Extraction 4. Feature Selection 5. Classification(Random forest, SVC) 6. Post Processing(Evaluation) 7. Decision
  14. 14. Genetic algorithms (GAs) are adaptive methods which may be used to solve search and optimization problems. They are based on the genetic processes Inspired by the biological evolution process Uses concepts of “Natural Selection” and “Genetic Inheritance” (Darwin 1859) Originally developed by John Holland (1975)
  15. 15. Selection, Cross-over, Mutation Initial set of Random solutions Fitness, Statistics Stop Decision Steps In GA
  16. 16. • Character recognition • Handwriting: processing checks • Bioinformatics Software's • Medical Diagnosis • Credit Card fraud • Pattern recognition: SNOOPE (bomb detector in U.S. airports)

Notas del editor

  • Transductionor transductive inference, tries to predict new outputs on specific and fixed (test) cases from observed, specific (training) cases.Semi-supervised learning combines both labeled and unlabeled examples to generate an appropriate function or classifier.
  • Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning.

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