B.E. (Automation and Robotics) 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:
- Introduction to Artificial Intelligence, Machine learning and Deep learning, Types of machine learning – Supervised, Unsupervised and Reinforcement learning
Module 2: Design of Machine Learning System:
- Collection of data, Data statistics – mean, variance, covariance, standard deviation, random variable, probability distribution function, data pre processing, data scaling, training of data, testing of data and its validation. Evaluation Metrics – Confusion matrix, precision, recall, F-score.
Module 3: Supervised Learning:
- Linear Regression, Multiple linear regression, Polynomial regression, Logistic Regression, Regularization techniques.
Module 4
- Unsupervised Learning and Classification: K-means and Hierarchical Clustering, Decision trees, Naïve-Bayes, SVM for linearly separable data, Kernel SVM for non-linearly separable data Dimensionality Reduction: LDA, Principal Component Analysis (PCA)
Module 5: Artificial Neural Networks:
- The Neurons and the Brain, Neural Networks and Representation: Perceptron, Multilayer perceptron, Gradient Descent, back propagation.
Module 6
- Application of ML algorithms in Industrial Automation and Robotics: ML algorithms applied for Factory automation, autonomous cars, automated robotic arm, process control, Data screening, feature engineering, model design, limitations.
Text Books
- 1 Harrington, Peter. Machine learning in action. Simon and Schuster, 2012.
- 2 Zheng, Alice, and Amanda Casari. Feature engineering for machine learning: principles and techniques for data scientists. " O'Reilly Media, Inc.", 2018.
- 3 Jiang, Hui. Machine Learning Fundamentals: A Concise Introduction. Cambridge University Press, 2021.
- 4 Huyen, C. “Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications”, O'Reilly Media, 2022.
- 5 Gupta, Itisha, and Garima Nagpal. Artificial Intelligence and Expert Systems. Stylus Publishing, LLC, 2020.
- 1 Pandey, Yogendra Narayan, et al. Machine Learning in the Oil and Gas Industry. apress, Texas, 2020.
- 2 Bangert, Patrick, ed. Machine learning and data science in the oil and gas industry: Best practices, tools, and case studies. Gulf Professional Publishing, 2021.
- 3 Das, Santosh Kumar, et al., eds. Machine learning algorithms for industrial applications. Cham: Springer, 2021. 46
Reproduced from the University of Mumbai syllabus for B.E. (Automation and Robotics) under REV-2019 'C' Scheme, in force from the academic year 2023-24. 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.