Machine Learning - 15CS73 / 17CS73
COURSE DESCRIPTION
Machine Learning is concerned with computer programs that automatically improve their performance through experience. This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as Bayesian networks, decision tree learning, statistical learning methods, unsupervised learning and reinforcement learning. The course covers theoretical concepts such as inductive bias, Bayesian learning methods. Short programming assignments include hands-on experiments with various learning algorithms. This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning.
Machine Learning is concerned with computer programs that automatically improve their performance through experience. This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as Bayesian networks, decision tree learning, statistical learning methods, unsupervised learning and reinforcement learning. The course covers theoretical concepts such as inductive bias, Bayesian learning methods. Short programming assignments include hands-on experiments with various learning algorithms. This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning.
COURSE OBJECTIVES
This course will enable students to,
COURSE OUTCOMES
After studying this course, the students will be able to
This course will enable students to,
- Define machine learning and understand the basic theory underlying machine learning.
- Differentiate supervised, unsupervised and reinforcement learning
- Understand the basic concepts of learning and decision trees.
- Understand neural networks and Bayesian techniques for problems appear in machine learning
- Understand the instant based learning and reinforced learning
- Perform statistical analysis of machine learning techniques.
COURSE OUTCOMES
After studying this course, the students will be able to
- Choose the learning techniques and investigate concept learning
- Identify the characteristics of decision tree and solve problems associated with
- Apply effectively neural networks for appropriate applications
- Apply Bayesian techniques and derive effectively learning rules
- Evaluate hypothesis and investigate instant based learning and reinforced learning
CONTENT
Module – 1 Introduction, Concept Learning
Module – 2 Decision Tree Learning
Module – 3 Artificial Neural Networks
Module – 4 Bayesian Learning
Module – 5 Evaluating Hypothesis, Instance Based Learning, Reinforcement Learning
Module – 1 Introduction, Concept Learning
Module – 2 Decision Tree Learning
Module – 3 Artificial Neural Networks
Module – 4 Bayesian Learning
Module – 5 Evaluating Hypothesis, Instance Based Learning, Reinforcement Learning
STUDY MATERIAL
Module – 1
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Module – 2
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Module – 3
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Module – 4
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Module – 5
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SOLUTIONS FOR SUPERVISED AND UNSUPERVISED LEARNING PROBLEMS
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QUESTION PAPER
Model_Question_Paper_15CS73 |
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REFERENCE
1. Tom M. Mitchell, Machine Learning, India Edition 2013, McGraw Hill Education.
2. http://www.cs.cmu.edu/~tom/mlbook-chapter-slides.html
3. http://www.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/mlbook.html
1. Tom M. Mitchell, Machine Learning, India Edition 2013, McGraw Hill Education.
2. http://www.cs.cmu.edu/~tom/mlbook-chapter-slides.html
3. http://www.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/mlbook.html