Machine learning
- 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. 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. 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. 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. 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. 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. Approaches • Decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the item's 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. 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. 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. Approaches • It is a machine learning technique used to optimize a population of computer programs according to a fitness landscape determined by a program's 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. 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. 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. 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. 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.