B.Sc. (Data Science) Machine Learning Syllabus - Mumbai University
This is the SY BSc Data Science syllabus under NEP 2020, in force from the academic year 2025-26. The University still sets the earlier Choice Based papers alongside it for ATKT candidates, so check which scheme your exam form names before you revise.
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Syllabus for Machine Learning
Module I: Introduction to Machine Learning: Machine Learning (ML), Need for Machine Learning, ML from Knowledge-driven to Data Driven, Applications
- of Machine Learning, Machine learning process, Problems suitable for Machine Learning, Advantages, Disadvantages and Challenges of Machine Learning, Challenges of ML. General architecture of ML systems, Types of Machine Learning: Supervised and Unsupervised Learning, Workflow, Semi-supervised Learning, Reinforced Learning.
- Underlying Concepts in Machine Learning: Inductive Learning, Generalization, Bias and Variance, Overfitting and Underfitting, Paramedic and Non-Parametric algorithms Classification Algorithms: Concept of Classification, Binary classification, Multi-Class Classification, Multi Label Classification K-Nearest Neighbour Method: need and Working of KNN, Computing Distance, Pros and Cons of KNN, Decision Tree based Algorithm: Terminologies and assumptions, Working of Decision Trees, ID3 Algorithm, Attribute selection Methods (Entropy, Gini Impurity, Information Gain) Performance Evaluation: Classification Metrics-Accuracy, Sensitivity, Precision, F1 Score, ROC/AUC Curve, Cross Validation
Module II: Support Vector Machines: Working of SVM, SVM Concepts-Support
- Vectors, Hard Margin, soft Margin, Kernels, Advantages and Disadvantages of SVM Probabilistic Learning: Introduction to Bayes Learning, Interpretation of Bayes Rule, Benefits and shortfalls of Bayesian Learning, Naïve Bayes Classifier, Characteristics of Naïve Bayes Regression Methods: Linear Regression Models, Logistic Regression Unsupervised Learning: Concept of unsupervised Learning, Importance and Challenges of unsupervised Learning, Clustering and its applications Hierarchical Clustering: Introduction, Types of Hierarchical Clustering, Issues with Hierarchical Clustering.
- Partition algorithm: K-means Clustering, steps of K-means Clustering, Issues, Strength and Weakness of K-means clustering. Curse of Dimensionality.
- Dimensionality Reduction: Criteria for Reduction, Feature Reduction and Selection, Principal Component Analysis(PCA) Association rule Mining: Basic Concepts, Market Basket Analysis, and Apriori algorithm.
Textbooks
- 1 Machine Learning: Concepts, Techniques and Applications, T V Geetha S, Sendhilkumar, CRC Press, Taylor and Francis, 1st Edition, 2023
- 1 Machine Learning for Decision Sciences with Case Studies in Python, S. Sumathi, Suresh V. Rajappa, CRC Press, Taylor and Francis 1st Edition 2022
- 2 Machine Learning by S Sridhar Oxford University Press, 1st Edition 2021
Reproduced from the University of Mumbai syllabus for B.Sc. (Data Science) under NEP 2020, in force from the academic year 2025-26. 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.