Machine Learning detailed syllabus scheme for Information Technology (IT), 2019-20 onwards has been taken from the DBATU official website and presented for the Bachelor of Technology students. For Subject Code, Course Title, Lecutres, Tutorials, Practice, Credits, and other information, do visit full semester subjects post given below.
For 8th Sem Scheme of Information Technology (IT), 2019-20 Onwards, do visit IT 8th Sem Scheme, 2019-20 Onwards. For the Elective-XIII scheme of 8th Sem 2019-20 onwards, refer to IT 8th Sem Elective-XIII Scheme 2019-20 Onwards. The detail syllabus for machine learning is as follows.
Machine Learning Syllabus for Information Technology (IT) 3rd Year 8th Sem 2019-20 DBATU
Course Objectives:
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Course Outcomes:
After learning the course the student will be able:
- To demonstrate knowledge of the machine learning literature
- To describe how and why machine learning methods work
- To demonstrate results of parameter selection
- To explain relative strengths and weaknesses of different machine learning methods
- To select and apply appropriate machine learning methods to a selected problem
- To implement machine learning algorithms on real datasets
- To suggest ways to improve results
Unit i
Introduction: Well-posed learning problems, Designing a Learning System, Perspectives and Issues in Machine learning Concept Learning and General-to-specific Ordering: A concept learning task, Concept learning as Search, Finding a maximally specific hypothesis, Version Spaces and Candidate elimination algorithm, Inductive Bias
Unit ii
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Unit iii
Bayesian Learning: Bayes theorem and concept learning, Maximum likelihood and least square error hypotheses, Minimum description length principle, Bayes optimal classifier, Gibbs algorithm, Naive Bayes classifier Computational Learning Theory: Probably learning an approximately correct hypothesis, PAC learnability, The VC dimension, the mistake bound model for learning
Unit iv
Linear Models for Regression: Linear basis function models, The Bias-Variance decomposition, Bayesian Linear Regression, Bayesian Model comparison Kernel Methods: Constructing kernels, Radial basis function networks, Gaussian Processes
Unit v
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Unit vi
Reinforcement Learning: The learning task, Q learning, Non-deterministic rewards and action, Temporal difference learning, Generalizing from examples
Text Books:
- Mitchell, Tom. M., Machine Learning, McGraw-Hill Education, 1st edition, May 2013.
- Segaran, Toby. Programming Collective Intelligence- Building Smart Web
- Applications, OReilly Media, August 2007.
Reference Book:
- Miroslav, Kubat. An Introduction to Machine Learning, Springer Publishing.
- Bishop, C. M., Pattern Recognition and Machine Learning, Springer Publishing.
- Conway, Drew and White, John Myles, Machine Learning for Hackers, O’Reilly Media, February 2012.
For detail syllabus of all subjects of Information Technology (IT) 8th Sem 2019-20 onwards, visit IT 8th Sem Subjects of 2019-20 Onwards.