Supervised learning and Unsupervised learning

Supervised learning and Unsupervised learning

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  ARTIFICIAL INTELLIGENT Supervised Learningand Unsupervised Learning
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  PRESENTED BY Hassan Fayyaz105 Usama Fayyaz 107 Maryam Arshad 097 Zeeshan Yousaf 113
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  MACHINE LEARNING Machine learningis a subfield of computer science that explores the study and construction of algorithms that can learn from 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.
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  TYPES OF MACHINELEARNING Supervised Learning Unsupervised Learning
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  SUPERVISED LEARNING Supervised learningis the task of inferring a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object and a desired output value. A supervised learning algorithm analyzes the training data and produces an inferred function.
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  SUPERVISED LEARNING PROCESSFLOW
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  SUPERVISED LEARNING PROCESS:TWO STEPS Learning (training): Learn a model using the training data Testing: Test the model using unseen test data to assess the model accuracy Accuracy= No. of correct classifications / Total no of test cases
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  SUPERVISED LEARNING Supervised learningproblems can be further grouped into regression and classification problems. • Classification: A classification problem is when the output variable is a category, such as “red” or “blue” or “disease” and “no disease”. • Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight”.
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  LIST OF COMMONSUPERVISED MACHINE LEARNING ALGORITHMS: • Decision Trees • K Nearest Neighbors • Linear SVC (Support vector Classifier) • Logistic Regression • Linear Regression
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  ADVANTAGES OF SUPERVISEDLEARNING  It allows you to be very specific about the definition of the labels.  You are able to determine the number of classes you want to have.  The input data is very well known and is labeled.  The results produced by the supervised method are more accurate.
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  DISADVANTAGES OF SUPERVISEDLEARNING  Supervised learning can be a complex method.  Supervised learning needed a lot of computation time for training  If you have a dynamic big and growing data, you are not sure of the labels to predefine the
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  UNSUPERVISED LEARNING Unsupervised learningis where you only have input data (X) and no corresponding output variables. The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. • These are called unsupervised learning because unlike supervised learning above there is no correct answers and there is no teacher. Algorithms are left to their own devises to discover and present the interesting structure in the data.
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  UNSUPERVISED LEARNING
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  UNSUPERVISED LEARNING Unsupervised learningproblems can be further grouped into clustering and association problems. Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior. Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y
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  LIST OF COMMONSUPERVISED MACHINE LEARNING ALGORITHMS: • K-means clustering • K-NN (k nearest neighbors) • Dimensionality Reduction • Hierarchical clustering
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  ADVANTAGES OF SUPERVISEDLEARNING  Less complexity in comparison with supervised learning.  It is often easier to get unlabeled data.  Takes place in real time such that all the input data to be analyzed and labeled in the presence of learners.
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  DISADVANTAGES OF SUPERVISEDLEARNING  You cannot get very specific about the definition of the data sorting and the output.  Less accuracy of the results.  The results of the analysis cannot be ascertained.