Machine Learning•   Machine learning is a scientific discipline concerned with the design and development of    algorithms...
Machine Learning•   Machine learning is concerned with the development of algorithms allowing the machine to    generalize...
Machine learning,KDD and data                 mining•   These three terms are commonly confused.•   Machine learning also ...
Machine learning,KDD and data                 mining•   An uninformed method will easily be outperformed by supervised met...
Theory•   The computational analysis of machine learning algorithms and their performance is a    branch of theoretical co...
Theory•   Negative results show that certain classes cannot be learned in polynomial time.•   There are many similarities ...
Approaches•   Decision tree learning uses a decision tree as a predictive model which maps    observations about an item t...
Approaches•   Algorithm is usually called ` neural network .•   Functional aspects of biological neural networks. Com puta...
Approaches•   They are usually used to model complex relationships between inputs and outputs.•   Genetic programming is a...
Approaches•   It is a machine learning technique used to optimize a population of computer programs    according to a fitn...
Approaches•   Support vector machines are a set of related supervised learning methods used for    classification and regr...
Approaches•   Two categories are an SVM training.•   Cluster analysis or clustering is the assignment of a set of observat...
Approaches•   A Bayesian network could represent the probabilistic relationships between diseases and    symptoms for exam...
Approaches•   Efficient algorithms exist that learning.•   Reinforcement learning is concerned with how an agent ought to ...
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Machine learning

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Machine learning

  1. 1. Machine Learning• Machine learning is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors.• Machine learning is a branch of artificial intelligence.• Machine learning is concerned with the development of algorithms allowing the machine to learn via inductive inference based on observing data that represents incomplete information about statistical phenomenon.
  2. 2. Machine Learning• Machine learning is concerned with the development of algorithms allowing the machine to generalize it to rules.• Machine learning is concerned with the development of algorithms allowing the machine to make predictions on missing attributes or future data.
  3. 3. Machine learning,KDD and data mining• These three terms are commonly confused.• Machine learning also employs data mining methods as ` unsupervised learning or as a preprocessing step to improve learner accuracy on the other hand.• Much of the confusion between these two research communities -LRB- which do often have separate conferences and separate journals , ECML PKDD being a major exception -RRB- comes from the basic assumptions they work with : in machine learning , the performance is usually evaluated with respect to the ability to reproduce known knowledge , while in KDD the key task is the discovery of previously unknown knowledge .
  4. 4. Machine learning,KDD and data mining• An uninformed method will easily be outperformed by supervised methods.• Supervised methods cannot be used due to the unavailability of training data in a typical KDD task.
  5. 5. Theory• The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory.• A computation is considered feasible if it can be done in polynomial time in computational learning theory.• Positive results show that a certain class of functions can be learned in polynomial time.
  6. 6. Theory• Negative results show that certain classes cannot be learned in polynomial time.• There are many similarities between machine learning theory and statistics.• They use different terms.
  7. 7. Approaches• Decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value.• An artificial neural network learning algorithm is a learning algorithm that is inspired by the structure.• Functional aspects of biological neural networks. Com putations are structured in terms of an interconnected group of artificial neurons.
  8. 8. Approaches• Algorithm is usually called ` neural network .• Functional aspects of biological neural networks. Com putations processes information using a connectionist approach to computation.• Modern neural networks are non-linear statistical data modeling tools.
  9. 9. Approaches• They are usually used to model complex relationships between inputs and outputs.• Genetic programming is an evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user-defined task.• It is a specialization of genetic algorithms where each individual is a computer program.
  10. 10. Approaches• It is a machine learning technique used to optimize a population of computer programs according to a fitness landscape determined by a programs ability to perform a given computational task.• Nductive logic programming is an approach to rule learning using logic programming as a uniform representation for examples, background knowledge, and hypotheses.• An ILP system will derive a hypothesized logic program which entails all the positive and none of the negative examples given an encoding of the known background knowledge and a set of examples represented as a logical database of facts.
  11. 11. Approaches• Support vector machines are a set of related supervised learning methods used for classification and regression.• Each marked as belonging to one of two categories algorithm builds a model that predicts whether a new example falls into one category or the other given a set of training examples.• A new example falls into one category or the other.
  12. 12. Approaches• Two categories are an SVM training.• Cluster analysis or clustering is the assignment of a set of observations into subsets so that observations in the same cluster are similar in some sense.• A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independencies via a directed acyclic graph.
  13. 13. Approaches• A Bayesian network could represent the probabilistic relationships between diseases and symptoms for example.• The network can be used to compute the probabilities of the presence of various diseases given symptoms.• Efficient algorithms exist that perform inference.
  14. 14. Approaches• Efficient algorithms exist that learning.• Reinforcement learning is concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward.• Reinforcement learning algorithms attempt to find a policy that maps states of the world to the actions the agent ought to take in those states.• Reinforcement learning differs from the supervised learning problem in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected.
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