05-10-2016, 11:01 AM
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ABSTRACT:-
Machine learning is a subfield of computer science that evolved from the study of patternrecognition and computational learning theory in artificial intelligenceIn 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed"Machine learning explores the study and construction of algorithms that can learnfrom and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions.
"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"This definition is notable for its defining machine learning in fundamentally operational rather than cognitive terms, thus following Alan Turing's proposal in his paper "Computing Machinery and Intelligence" that the question "Can machines think?" be replaced with the question "Can machines do what we (as thinking entities) can do?"[
Why Machine Learning is important
We cannot program everything.Some tasks are difficult to define algorithmically Especially in computer vision ..visual sensing has few rules
Algorithms:Machine Learning algorithms can be organized based on the desired outcome of the algorithm or the type of input available during training the machine.
• Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.
• Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
• Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether it has come close to its goal. Another example is learning to play a game by playing against an opponent
Some real life applications of machine learning:
• Recommender systems - suggesting similar people on Facebook/Linkedln, similar movies/ books etc. on Amazon
• Business applications - Customer segmentation, Customer retention, Targeted Marketing etc.
• Medical applications - Disease diagnosis
• Banking - Credit card issue, fraud detection etc
• Language translation-text to speech or vice versa.
Conclusion:
In future the study of machine learning holds exciting prospects with constant innovations in diverse fields. With better algorithms,we can completely bridge the gap between men and machines.Because it is a type of adaptive learning it will find applications in possible fields.