Machine Learning Algorithms detailed syllabus for Industrial Engineering (Industrial) for 2019 regulation curriculum has been taken from the Anna Universities official website and presented for the Industrial students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.
For Industrial Engineering 6th Sem scheme and its subjects, do visit Industrial 6th Sem 2019 regulation scheme. For Professional Elective-II scheme and its subjects refer to Industrial Professional Elective-II syllabus scheme. The detailed syllabus of machine learning algorithms is as follows.
Course Objective:
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 pdfs platform to make students’s lives easier.
.
Unit I
Concept Learning
A Concept Learning Task: Notation, The Inductive Learning Hypothesis,Concept Learning as Search, FIND-S: Algorithm for finding a Maximally Specific Hypothesis: Version Spaces and the CANDIDATE-ELIMINATION Algorithm; Convergence of CANDIDATE-ELIMINATION Algorithm to the correct Hypothesis; Appropriate Training Examples for learning; Applying Partially Learned Concept, Inductive Bias: A Biased Hypothesis Space; An Unbiased Learner; The Futility of Bias-Free Learning.
Unit II
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 pdfs platform to make students’s lives easier..
Unit III
Evaluating Hypotheses
Estimating Hypothesis Accuracy: Sample Error and True Error; Confidence Intervals for DiscreteValued Hypotheses. Basics of Sampling Theory: Error Estimation and Estimating Binomial Proportions; the Binomial Distribution; Mean and Variance; Estimators, Bias; and Variance; Confidence Intervals; Two-sided and one-sided bounds. A General approach for deriving confidence intervals: Central Limit Theorem. Difference in Error of two hypotheses; Hypothesis Testing. Comparing Learning Algorithms: Paired t Tests; Practical Considerations.
Unit IV
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 pdfs platform to make students’s lives easier..
Unit V
Computational Learning Theory
Introduction, probably learning an approximately correct hypothesis: The Problem Setting; Error of a Hypothesis; Learnability. Sample Complexity for Finite Hypothesis Spaces: Agnostic Learning and Inconsistent Hypotheses; Conjunctions of Boolean earnability of Other Concept Classes. Sample Complexity for infinite hypothesis spaces: Shattering a set of Instances; The Vapnik-Chervonenkis Dimension; Sample Complexity and the VC Dimension. The mistake bound model of learning: Mistake bound for the FIND-S Algorithm; Mistake bound for the HALVING Algorithm; Optimal Mistake Bounds; WEIGHTED-MAJORITY Algorithm.
Course Outcome:
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 pdfs platform to make students’s lives easier..
Text Books:
- Machine Learning by Tom M. Mitchell ,McGraw-Hill International Edition, 1997
For detailed syllabus of all the other subjects of Industrial Engineering 6th Sem, visit Industrial 6th Sem subject syllabuses for 2019 regulation.
For all Industrial Engineering results, visit Anna University Industrial all semester results direct link.