B.E. (Computer Engineering) Machine Learning Syllabus - Mumbai University 2026
The University has moved this degree onto NEP 2020 one year at a time. The first and second years are NEP 2020 syllabi; the third and fourth years are still examined on the REV-2019 'C' Scheme, which is what the University sets for them this year.
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Syllabus for Machine Learning
Module 1: Introduction to Machine Learning
- Machine Learning, Types of Machine Learning, Issues in Machine
- 1.1 Learning, Application of Machine Learning, Steps in developing a Machine Learning Application. Training Error, Generalization error, Overfitting, Underfitting, Bias 1.2 Variance trade-off.
Module 2: Learning with Regression and Trees
- Learning with Regression: Linear Regression, Multivariate Linear 2.1 Regression, Logistic Regression. Learning with Trees: Decision Trees, Constructing Decision Trees using 2.2 Gini Index (Regression), Classification and Regression Trees (CART) Performance Metrics: Confusion Matrix, [Kappa Statistics], Sensitivity, 2.3 Specificity, Precision, Recall, F-measure, ROC curve
Module 3: Ensemble Learning
- Understanding Ensembles, K-fold cross validation, Boosting, Stumping, 3.1 XGBoost Bagging, Subagging, Random Forest, Comparison with Boosting, 3.2 Different ways to combine classifiers
Module 4: Learning with Classification
- Support Vector Machine Constrained Optimization, Optimal decision boundary, Margins and 4.1 support vectors, SVM as constrained optimization problem, Quadratic Programming, SVM for linear and nonlinear classification, Basics of Kernel trick.
- 4.2 Support Vector Regression, Multiclass Classification
Module 5: Learning with Clustering
- Introduction to clustering with overview of distance metrics and major 5.1 clustering approaches. Graph Based Clustering: Clustering with minimal spanning tree
- 5.2 Model based Clustering: Expectation Maximization Algorithm, Density Based Clustering: DBSCAN
Module 6: Dimensionality Reduction
- Dimensionality Reduction Techniques, Principal Component Analysis, 6.1 Linear Discriminant Analysis, Singular Valued Decomposition. Total 39
Textbooks
- 1 Peter Harrington, “Machine Learning n Action”, DreamTech Press
- 2 Ethem Alpaydın, “Introduction to Machine Learning”, MIT Press
- 3 Tom M. Mitchell, “Machine Learning” McGraw Hill
- 4 Stephen Marsland, “Machine Learning An Algorithmic Perspective”, CRC Press
- 1 Han Kamber, ―Data Mining Concepts and Techniques‖, Morgan Kaufmann Publishers Margaret. H. Dunham, ―Data Mining Introductory and Advanced Topics, Pearson 2 Education
- 3 Kevin P. Murphy , Machine Learning ― A Probabilistic Perspective‖
- 4 Samir Roy and Chakraborty, ―Introduction to soft computing‖, Pearson Edition. Richard Duda, Peter Hart, David G. Stork, “Pattern Classification”, Second Edition, Wiley 5 Publications.
Reproduced from the University of Mumbai syllabus for B.E. (Computer Engineering) under REV-2019 'C' Scheme, in force from the academic year 2022-23. Wording is as printed in that syllabus. Module numbering is as printed there too.
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.