IT702: Machine Learning Syllabus for IT 7th Sem 2020-21 DBATU

Machine Learning detailed syllabus scheme for B.Tech Information Technology (IT), 2020-21 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 all other DBATU Syllabus for Information Technology 7th Sem 2020-21, do visit IT 7th Sem 2020-21 Onwards Scheme. The detailed syllabus scheme for machine learning is as follows.

Machine Learning Syllabus for Information Technology (IT) 4th Year 7th Sem 2020-21 DBATU

Prerequisites:

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Course Objectives:

1. To understand the basic concepts and methods of machine learning.
2. To make use of some elementary machine learning techniques in the design of computer systems.
3. To develop a broad perspective about the applicability of ML algorithms in different fields.
4. To understand the major machine learning algorithms, the problem settings and assumptions that underlies them.
5. To possess insights, concerning the relative strengths and weaknesses of various common machine learning methods.

Course Outcomes:

After learning the course the student will be able:

1. To demonstrate knowledge of the machine learning literature.
2. To describe how and why machine learning methods work.
3. To demonstrate results of parameter selection.
4. To explain relative strengths and weaknesses of different machine learning methods.
5. To select and apply appropriate machine learning methods to a selected problem.
6. To implement machine learning algorithms on real datasets.
7. To suggest ways to improve results.

UNIT I

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UNIT II

Decision Tree Learning: Decision tree learning algorithm, Hypothesis space search in decision tree Evaluating Hypothesis: Estimating Hypothesis accuracy, Basics of sampling theory, Deriving confidence intervals, Hypothesis testing, comparing learning algorithms.

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

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UNIT V

Unsupervised Learning: Clustering:Learning from unclassified data, Hierarchical Aglomerative Clustering, k-means partitional clustering, Batchler and Wilkin’s algorithm.

UNIT VI

Reinforcement Learning: The learning task, Q learning, Non-deterministic rewards and action, Temporal difference learning, Generalizing from examples.

Text Books:

1. Mitchell, Tom. M., Machine Learning, McGraw-Hill Education, 1st Edition, May 2013.
2. Segaran, Toby. Programming Collective Intelligence- Building Smart Web 2.0 Applications, OReilly Media, August 2007.

Reference Books:

For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdf platform to make students’s lives easier.
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For detail syllabus of all other subjects of Information Technology (IT) 7th Sem 2020-21 regulation, visit IT 7th Sem Subjects syllabus for 2020-21 regulation.