Machine learning
- 2. Machine learning Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “Training Data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in the applications of email filtering, detection of network intruders and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning. In its application across business problems Machine learning is the study of computer systems that learn from data and experience. It is applied in an incredibly wide variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to
- 3. Why learn? • Understand and improve efficiency of human learning – Improve methods for teaching and tutoring people (better CAI) • Discover new things or structure that were previously unknown to humans – Examples: data mining, scientific discovery • Fill in skeletal or incomplete specifications about a domain – Large, complex AI systems cannot be completely derived by hand and require dynamic updating to incorporate new information. – Learning new characteristics expands the domain or expertise and lessens the “brittleness” of the system • Build software agents that can adapt to their users or to other software agents • Reproduce an important aspect of intelligent behavior
- 5. Some Major Paradigms Of Machine Learning • Rote learning – Hand-encoded mapping from inputs to stored representation. “Learning by memorization.” • Interactive learning – Human/system interaction producing explicit mapping. • Induction – Using specific examples to reach general conclusions. • Analogy – Determining correspondence between two different representations. Case-based reasoning • Clustering – Unsupervised identification of natural groups in data • Discovery – Unsupervised, specific goal not given • Genetic algorithms – “Evolutionary” search techniques, based on an analogy to “survival of the fittest”
- 6. Elements of Machine Learning 1.Generalization :- How well a model performs on newdata. 2.Data:- Training data: specific examples to learn from. Test data: new specific examples to assess performance. 3.Models(theoretical assumptions):- decision trees, naive bayes, perceptron, etc. 4.Algorithms:- Learning algorithms that infer the model parameters from the data. Inference algorithms that infer prediction from a model. Machine Learning & Statistics Machine learning and statistics are closely related fields. According to Michael Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistic. He also suggested the term data science as a placeholder to call the overall field.
- 7. There are several algorithms for Machine learning 1.Decision Tree Algorithm. 2. Bayesian Classification Algorithm. 3. Shortest Path Calculation Algorithm. 4. Neural Network Algorithm. 5. Genetic Algorithm • A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data; the difficulty lies in the fact that the set of all possible behaviors given all possible inputs is too large to be covered by the set of observed examples (training data). Hence the learner must generalize from the given examples, so as to be able to produce a useful output in new cases. Some machine learning systems attempt to eliminate the need for human interaction in data analysis, while others adopt a collaborative approach between human and machine. Human intuition cannot, however, be entirely eliminated, since the system's designer must specify how the data is to be represented and what mechanisms will be used to search for a characterization of the data.
- 9. Necessity of Human Machine Interfacing • Number of types of obstacles in real world are huge, hence it has to proceed to a generalize solution using certain set of rules. • Only disadvantage being its high error making. • And thus human interfacing with these systems becomes necessary.