B.E. (Biomedical Engineering) Machine Learning Abbreviated As ML Syllabus - Mumbai University
This is the Fourth Year Biomedical Rev 2019 C Scheme syllabus under REV-2019 'C' Scheme, in force from the academic year 2022-23. The University has not yet published an NEP 2020 syllabus above Semester IV for this branch, and this is the scheme its examinations are set on.
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Syllabus for Machine Learning (Abbreviated as ML)
Module 2: Clustering: Introduction, mixture densities, k-means clustering, expectation maximization algorithm, supervised learning after clustering, spectral clustering, hierarchical clustering, choosing the number of clusters.
- Decision Trees: Introduction, univariate trees, rule extraction from trees, learning rules from data, multivariate trees, clustering, overfitting and evaluation
Module 3
- Design and analysis of machine learning experiments: Introduction, factors, response, and strategy of experimentation, response surface design, randomization, replication, and blocking, guidelines for machine learning experiments, cross validation and resampling methods, measuring classifier performance
Module 4
- Introduction of artificial neural networks: structure and function of a biological neurons, artificial neuron models, concepts of neural network, single layer and multilayer preceptor, structure of an ANN, feed-forward neural network, gradient descent, back propagation, architectures of neural networks, optimisation of neural network model
Module 5
- Architecture and training the ANN: Type of learning the neural network, training of a single-layer neural network, delta rule, designing ANN models, radial basis function, overview of learning rules and parameters, activation functions, multilayer feed forward network, backpropagation networks, architecture, radial basis function network
Module 6: Fundamentals of deep networks: Deep Learning, architectural principles of deep networks, parameters, layers, loss function , optimization algorithms, hyper parameters, building blocks of deep networks, architectures of deep networks, convolutional neural networks, architecture of convolutional neural networks, input layers, convolutional layers, pooling layers, fully connected layers, recurrent neural
- networks, architecture of recurrent neural network
Text books
- 1 Introduction to Machine Learning, 3rd edition, Ethem Alpaydin, PHI Learning Private Limited, New Delhi, 2015
- 2 Deep Learning by Josh Patterson and Adam Gibson, O’Reilly Media, Inc., Gravenstein Highway North, Sebastopol CA, 2017
- 3 Neural Networks and Learning Machines Third Edition, Simon Haykin, Pearson, Prentice Hall, 2009
- 1 Machine learning in Action, Peter Harrington, dreamtech Press, New Delhi, 2012
- 2 Machine learning, Tom M, Mitchell, Mc Graw Hill Education(India) Private Limited New Delhi, 2013 University of Mumbai, Biomedical Engineering, Rev 2022-23 14
- 1 Course 1: Introduction to Machine Learning https://nptel.ac.in/courses/1061061392.
- 2 Course 2: Introduction to Machine Learning https://onlinecourses.nptel.ac.in/noc22_cs29/preview Theory Examination:
Reproduced from the University of Mumbai syllabus for B.E. (Biomedical 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
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