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
- 1.1 Machine Learning, Types of Machine Learning, Issues in Machine Learning, Application of Machine Learning, Steps in developing a Machine Learning Application.
- 1.2 Training Error, Generalization error, Overfitting, Underfitting, Bias-Variance trade-off.
Module 2: Learning with Regression and Trees
- 2.1 Learning with Regression: Linear Regression, Multivariate Linear Regression, Logistic Regression.
- 2.2 Learning with Trees: Decision Trees, Constructing Decision Trees using Gini Index (Regression), Classification and Regression Trees (CART)
- 2.3 Performance Metrics: Confusion Matrix, [Kappa Statistics], Sensitivity, Specificity, Precision, Recall, F-measure, ROC curve
Module 3: Ensemble Learning
- 3.1 Understanding Ensembles, K-fold cross validation, Boosting, Stumping, XGBoost
- 3.2 Bagging, Subagging, Random Forest, Comparison with Boosting, Different ways to combine classifiers
Module 4: Learning with Classification
- 4.1 Support Vector Machine Constrained Optimization, Optimal decision boundary, Margins and 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
- 5.1 Introduction to clustering with overview of distance metrics and major clustering approaches.
- 5.2 Graph Based Clustering: Clustering with minimal spanning tree Model based Clustering: Expectation Maximization Algorithm, Density Based Clustering: DBSCAN
Module 6: Dimensionality Reduction
- 6.1 Dimensionality Reduction Techniques, Principal Component Analysis, 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
- 2 Margaret. H. Dunham, ―Data Mining Introductory and Advanced Topics, Pearson Education
- 3 Kevin P. Murphy , Machine Learning ― A Probabilistic Perspective‖
- 4 Samir Roy and Chakraborty, ―Introduction to soft computing‖, Pearson Edition.
- 5 Richard Duda, Peter Hart, David G. Stork, “Pattern Classification”, Second Edition, Wiley 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.