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Home / Syllabus / B.E. (Artificial Intelligence and Data Science) / TY BE AI and DS / Semester 6 / Machine Learning

B.E. (Artificial Intelligence and Data Science) Machine Learning Syllabus - Mumbai University 2026

This is the TY BE AI and DS syllabus under CBCS REV-2019 'C' Scheme, in force from the academic year 2022-23. 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 1T01815 and 1T01816. The first and second years of the degree are on NEP 2020.

Machine Learning.pdf
Semester 6 · TY BE AI and DS · 3 credits · CBCS REV-2019 'C' Scheme

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Syllabus for Machine Learning

Semester 6 · TY BE AI and DS · 3 credits · CBCS REV-2019 'C' Scheme

Module 1 6 hours

  • Introduction to Machine Learning
  • 1.1 Introduction to Machine Learning, Issues in Machine Learning, Application of Machine Learning, Steps of developing a Machine Learning Application.
  • Supervised and Unsupervised Learning: Concepts of Classification, Clustering and prediction, Training, Testing and validation dataset, cross validation, overfitting and underfitting of model
  • Performance Measures: Measuring Quality of model- Confusion Matrix, Accuracy, Recall, Precision, Specificity, F1 Score, RMSE

Module 2 5 hours

  • Mathematical Foundation for ML
  • 2.1 System of Linear equations, Norms, Inner products, Length of Vector, Distance between vectors, Orthogonal vectors
  • 2.2 Symmetric Positive Definite Matrices, Determinant, Trace, Eigenvalues and vectors, Orthogonal Projections, Diagonalization, SVD and its applications

Module 3 7 hours

  • Linear Models
  • 3.1 The least-squares method, Multivariate Linear Regression, Regularized Regression, Using Least-Squares Regression for classification
  • 3.2 Support Vector Machines

Module 4 4 hours

  • Clustering
  • 4.1 Hebbian Learning rule
  • 4.2 Expectation -Maximization algorithm for clustering

Module 5 10 hours

  • Classification models
  • 5.1 Introduction, Fundamental concept, Evolution of Neural Networks, Biological Neuron, Artificial Neural Networks, NN architecture, McCulloch-Pitts Model. Designing a simple network, Non-separable patterns, Perceptron model with Bias. Activation functions, Binary, Bipolar, continuous, Ramp. Limitations of Perceptron.
  • 5.2 Perceptron Learning Rule. Delta Learning Rule (LMS-Widrow Hoff), Multi-layer perceptron network. Adjusting weights of hidden layers. Error back propagation algorithm.
  • 5.3 Logistic regression

Module 6 07 hours

  • Dimensionality Reduction
  • 6.1 Curse of Dimensionality.
  • 6.2 Feature Selection and Feature Extraction
  • 6.3 Dimensionality Reduction Techniques, Principal Component Analysis.

Text Books

  • 1 Nathalie Japkowicz & Mohak Shah, "Evaluating Learning Algorithms: A Classification Perspective", Cambridge.
  • 2 Marc Peter Deisenroth, Aldo Faisal, Cheng Soon Ong, "Mathematics for machine learning",
  • 3 Samir Roy and Chakraborty, "Introduction to soft computing", Pearson Edition.
  • 4 Ethem Alpaydın, "Introduction to Machine Learning", MIT Press McGraw-Hill Higher Education
  • 5 Peter Flach, "Machine Learning", Cambridge University Press

References

  • 1 Tom M. Mitchell, "Machine Learning", McGraw Hill
  • 2 Kevin P. Murphy, "Machine Learning " A Probabilistic Perspective", MIT Press
  • 3 Stephen Marsland, "Machine Learning an Algorithmic Perspective", CRC Press
  • 4 Shai Shalev-Shwartz, Shai Ben-David, "Understanding Machine Learning", Cambridge University Press
  • 5 Peter Harrington, "Machine Learning in Action", DreamTech Press

Useful Links

  • 1 NPTEL
  • 2 AI and ML Certification - Enroll in PGP AI ML Courses with Purdue (simplilearn.com)
  • 3 https://www.learndatasci.com/out/coursera-machine-learning/
  • 4 https://www.learndatasci.com/out/google-machine-learning-crash-course/

Reproduced from the University of Mumbai syllabus for B.E. (Artificial Intelligence and Data Science), item 6.42 (R), under CBCS REV-2019 'C' Scheme, in force from the academic year 2022-23. 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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