Introduction to Machine Learning for Beginners
- 2. 2 2 Objectives 1) What is Learning? 2) What is Machine Learning? 3) Steps in machine learning. 4) Types of machine Learning. 5) Applications of Machine Learning. Applications of Machine Learning.
- 3. 3 What is Learning? “To gain knowledge or understanding of, or skill in by study, instruction or experience'' Learning a set of new facts. Learning HOW to do something . Improving ability of something already learned.
- 4. 4 4 What is Machine Learning? Machine Learning is the study of methods for programming computers to learn. Building machines that automatically learn from experience. Machine learning usually refers to the changes in systems that perform tasks associated with artificial intelligence AI Such tasks involve recognition, diagnosis, planning, robot control, prediction, etc.
- 5. 5 What is Machine Learning? Learning algorithm TRAINING DATA Answer Trained machine Query
- 6. 6 6 Steps in machine learning 1) Data collection. 2) Representation. 3) Modeling. 4) Estimation. 5) Validation. 6) Apply learned model to new “test” data
- 7. 7 Learning system Learning system Learning Process Problem Solving Performance Evaluation Results Teacher Feed-back Data General structure of a learning system
- 8. 8 8 Advantages of ML 1) Solving vision problems through statistical inference. 2) Intelligence from the common sense AI. 3) Reducing the constraints over time achieving complete autonomy.
- 9. 9 9 9 1) Application specific algorithms. 2) Real world problems have too many variables and sensors might be too noisy. 3) Computational complexity. Disadvantages of ML
- 10. 10 10 Types of machine Learning 1) Unsupervised Learning . 2) Semi-Supervised (reinforcement). 3) Supervised Learning.
- 11. 11 11 Unsupervised Learning Studies how input patterns can be represented to reflect the statistical structure of the overall collection of input patterns No outputs are used (unlike supervised learning and reinforcement learning) Learner is provided only unlabeled data. No feedback is provided from the environment
- 12. 12 12 Unsupervised Learning Advantage Most of the laws of science were developed through unsupervised learning. Disadvantage The identification of the features itself is a complex problem in many situations.
- 13. 13 13 Semi-Supervised (reinforcement) it is in between Supervised and Unsupervised learning techniques the amount of labeled and unlabelled data required for training. With the goal of reducing the amount of supervision required compared to supervised learning. At the same time improving the results of unsupervised clustering to the expectations of the user.
- 14. 14 14 Semi-Supervised (reinforcement) Semi-supervised learning is an area of increasing importance in Machine Learning. Automatic methods of collecting data make it more important than ever to develop methods to make use of unlabeled data.
- 15. 15 15 Supervised Learning 1) Analogical Learning. 2) Learning by Decision Tree.
- 16. 16 Analogical Learning instances of a problem and the learner has to form a concept that supports most of the positive and no negative instances. This demonstrates that a number of training instances are required to form a concept in inductive learning. Unlike this, analogical learning can be accomplished from a single example. For instance, given the following training instance, one has to determine the plural form of bacilus. 16
- 18. 18 The main steps in analogical learning are now formalized below. 1. Identifying Analogy: Identify the similarity between an experienced problem instance and a new problem. 2. Determining the Mapping Function: Relevant parts of the experienced problem are selected and the mapping is determined. 3. Apply Mapping Function: Apply the mapping function to transform the new problem from the given domain to the target domain. 18
- 19. 19 The main steps in analogical learning are now formalized below. 4. Validation: The newly constructed solution is validated for its applicability through its trial processes like theorem or simulation . 5. Learning: If the validation is found to work well, the new knowledge is encoded and saved for future usage. 19
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- 21. 21 Learning by Decision Tree A decision tree receives a set of attributes (or properties) of the objects as inputs and yields a binary decision of true or false values as output. Decision trees, thus, generally represent Boolean functions. Besides a range of {0,1} other non-binary ranges of outputs are also allowed. However, for the sake of simplicity, we presume the restriction to Boolean outputs. Each node in a decision tree represents ‘a test of some attribute of the instance, and each branch descending from that node corresponds to one of the possible values for this attribute’ 21
- 22. 22 Learning by Decision Tree To illustrate the contribution of a decision tree, we consider a set of instances, some of which result in a true value for the decision. Those instances are called positive instances. On the other hand, when the resulting decision is false, we call the instance ‘a negative instance’. We now consider the learning problem of a bird’s flying. Suppose a child sees different instances of birds as tabulated below. 22
- 23. 23 Learning by Decision Tree 23
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- 31. 31 Radar Imaging