Machine learning :
Machine learning is that stream of computer science which works on statistical techniques which provide to programs of systems the ability to learn, test, improve, solve the problems at its own level without any need of human efforts.
Arthur Samuel has first introduced Machine Learning in 1959 during the study of pattern recognition and computational learning theory in Artificial Intelligence. Machine Learning explores the study and construction of Algorithms which manipulates the data and provides predictions on data which can be used for further decisions on computer systems.
Machine Learning concept totally ignored the static program instructions and on sample inputs and works only on dynamic elements.Machine Learning comes into that part or it is used where the designing and programming is difficult to perform well on it.It is widely used now a days and it’s most used applications are Email filtering , detection of network intruders and computer vision.
machine learning tasks:
Machine Learning tasks are generally categorised into two parts which depends on that there is a learning “signal” or “feedback” available to a learning system :
- 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. As special cases, the input signal can be only partially available, or restricted to special feedback:
- Semi-supervised learning:the computer is given only an incomplete training signal: a training set with some (often many) of the target outputs missing.
- Active learning :the computer can only obtain training labels for a limited set of instances (based on a budget), and also has to optimize its choice of objects to acquire labels for. When used interactively, these can be presented to the user for labeling.
- Reinforcement learning :training data (in form of rewards and punishments) is given only as feedback to the program’s actions in a dynamic environment, such as driving a vehicle or playing a game against an opponent.
- 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.
machine learning applications:
Another categorization of machine learning tasks arises when one considers the desired output of a machine-learned system:
In classification, inputs are divided into two or more classes, and the learner must produce a model that assigns unseen inputs to one or more (multi-label classification) of these classes. This is typically tackled in a supervised way. Spam filtering is an example of classification, where the inputs are email (or other) messages and the classes are “spam” and “not spam”.
In regression, also a supervised problem, the outputs are continuous rather than discrete.
In clustering, a set of inputs is to be divided into groups. Unlike in classification, the groups are not known beforehand, making this typically an unsupervised task.
Density estimation finds the distribution of inputs in some space.
Dimensionality reduction simplifies inputs by mapping them into a lower-dimensional space. Topic modeling is a related problem, where a program is given a list of human language documents and is tasked to find out which documents cover similar topics.
Among other categories of machine learning problems, learning to learn learns its own inductive bias based on previous experience. Developmental learning, elaborated for robot learning, generates its own sequences (also called curriculum) of learning situations to cumulatively acquire repertoires of novel skills through autonomous self-exploration and social interaction with human teachers and using guidance mechanisms such as active learning, maturation, motor synergies, and imitation.
machine learning vs Artificial intelligence (ai) :