MachineLearning_intro_Types_of_learning.pptx

types of machine learning

1. MACHINE LEARNING PCS 206 1

2. Overview Machine Learning Supervised Learning Unsupervised Learning 2

3. Goal of Artificial Intelligence Development of computers/machine that can be taught rather than programmed Simulation of intelligence Machine should be adaptable to new situations Capable of learning from experience 3

4. Machine Learning: Introduction Subfield of artificial intelligence (AI) Provides systems the ability to automatically learn and improve from experience without being explicitly programmed.  Machine learning focuses on the development of computer programs  that can access data and use it learn for themselves. 4

5. Learning is an important aspect of intelligence 5

6. Machine Learning begins with DATA The process of learning begins with observations or data Examples: direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide.  The primary aim is to allow the computers learn automatically   without human intervention or assistance and adjust actions accordingly. 6

7. Machine Learning Algorithms Machine Learning methods are broadly classified into following categories: 7

8. Supervised Learning: Training Data mining 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 (typically a vector) and the desired output value. 8

9. Supervised Learning…Contd. A  supervised learning algorithm  analyzes the training data Produces an inferred function This function is used for mapping new examples. 9

10. Supervised Learning…Testing An optimal scenario will allow for the algorithm to correctly determine the class labels for  unseen instances . This requires the learning algorithm to generalize from the training data to unseen situations in a “reasonable” way. Eg. classification and regression algorithms 10

11. Supervised Classification 11

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13. Unsupervised Learning The information used to train is neither classified nor labeled. U.L studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can differentiate the given input data. All clustering algorithms fall under supervised learning. 13