ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING - 18CS71
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,
- Explain Artificial Intelligence and Machine Learning and understand the basic theory underlying
- Understand the basic concepts of learning and decision trees
- Understand Neural networks and Bayesian techniques for problems that appear in machine learning
- Understand instant based learning and reinforced learning
- Illustrate AI and ML algorithms and their uses in the appropriate applications
COURSE OUTCOMES
After studying this course, the students will be able to
- Appraise the theory of Artificial intelligence and Machine Learning.
- Use knowledge representation and demonstrate the concept learning and learning techniques
- Demonstrate the characteristics of the decision tree, neural network and apply them effectively for appropriate applications.
- Demonstrate the concepts of Bayesian Learning and apply Bayesian techniques for solving real-world problems
- Evaluate hypothesis and investigate instant based learning and reinforced learning
CONTENT
Module – 1 Introduction, artificial intelligence
Module – 2 Knowledge representation, Concept Learning
Module – 3 Decision Tree Learning, Artificial Neural Networks
Module – 4 Bayesian Learning
Module – 5 Instance Based Learning, Reinforcement Learning
Module – 1 Introduction, artificial intelligence
Module – 2 Knowledge representation, Concept Learning
Module – 3 Decision Tree Learning, Artificial Neural Networks
Module – 4 Bayesian Learning
Module – 5 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