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B.E. (Artificial Intelligence and Data Science) Design of Experiments Syllabus - Mumbai University 2026

This is the Fourth Year BE AI and DS syllabus under CBCS REV-2019 'C' Scheme, in force from the academic year 2023-24. The University has published no NEP 2020 syllabus for Semesters V to VIII of any engineering branch, so this is the scheme you are examined on — exam form 1T01817 and 1T01818. The first and second years of the degree are on NEP 2020.

Design of Experiments.pdf
Semester 7 · Fourth Year BE AI and DS · 3 credits · CBCS REV-2019 'C' Scheme

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Syllabus for Design of Experiments

Semester 7 · Fourth Year BE AI and DS · 3 credits · CBCS REV-2019 'C' Scheme

Module 01 06 hours

  • Introduction Strategy of Experimentation Typical Applications of Experimental Design Guidelines for Designing Experiments Response Surface Methodology

Module 02 08 hours

  • Fitting Regression Models Linear Regression Models Estimation of the Parameters in Linear Regression Models Hypothesis Testing in Multiple Regression Confidence Intervals in Multiple Regression Prediction of new response observation Regression model diagnostics Testing for lack of fit

Module 03 07 hours

  • Two-Level Factorial Designs The 22 Design The 23 Design The General2k Design A Single Replicate of the 2k Design The Addition of Center Points to the 2k Design, Blocking in the 2k Factorial Design Split-Plot Designs

Module 04 07 hours

  • Two-Level Fractional Factorial Designs The One-Half Fraction of the 2k Design The One-Quarter Fraction of the 2k Design The General 2k-p Fractional Factorial Design Resolution III Designs Resolution IV and V Designs Fractional Factorial Split-Plot Designs

Module 05 07 hours

  • Response Surface Methods and Designs Introduction to Response Surface Methodology The Method of Steepest Ascent Analysis of a Second-Order Response Surface Experimental Designs for Fitting Response Surfaces

Module 06 04 hours

  • Taguchi Approach Crossed Array Designs and Signal-to-Noise Ratios Analysis Methods Robust design examples

References

  • 1 Raymond H. Mayers, Douglas C. Montgomery, Christine M. Anderson-Cook, Response Surface Methodology: Process and Product Optimization using Designed Experiment, 3rd edition,John Wiley & Sons, New York, 2001
  • 2 D.C. Montgomery, Design and Analysis of Experiments, 5th edition, John Wiley &Sons, New York, 2001
  • 3 George E P Box, J Stuart Hunter, William G Hunter, Statics for Experimenters: Design, Innovation and Discovery, 2nd Ed. Wiley
  • 4 W J Dimond, Peactical Experiment Designs for Engineers and Scintists, John Wiley and Sons Inc. ISBN: 0-471-39054-2
  • 5 Design and Analysis of Experiments (Springer text in Statistics), Springer by A.M. Dean,and D. T.Voss

Reproduced from the University of Mumbai syllabus for B.E. (Artificial Intelligence and Data Science), item 6.12 (N), under CBCS REV-2019 'C' Scheme, in force from the academic year 2023-24. Wording, module numbering and hours are as printed in that syllabus.

The complete syllabus

This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.

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PDF B.E. Artificial Intelligence and Data Science - Third Year, Semester V and VI - CBCS REV-2019 C Scheme - Item 6.42 (R) CBCS REV-2019 'C' Scheme, Semesters V and VI, in force from 2022-23 Read full PDF Read
PDF B.E. Artificial Intelligence and Data Science - Fourth Year, Semester VII and VIII - CBCS REV-2019 C Scheme - Item 6.12 (N) CBCS REV-2019 'C' Scheme, Semesters VII and VIII, in force from 2023-24 Read full PDF Read
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