B.E Artificial Intelligence and Machine Learning Machine Learning Lab Syllabus - Mumbai University
This is the Third Year AI-ML syllabus under REV-2019 'C' Scheme, in force from the academic year 2022-23. The University has not yet published an NEP 2020 syllabus for this year of the degree, and this is the scheme its examinations are set on.
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Syllabus for Machine Learning Lab
Module 1: Introduction to platforms such as Anaconda, COLAB
Module 2: Study of Machine Learning Libraries and tools (Python library, tensorflow, keras,...)
- Implementation of following algorithms for a given example data set
Module 3: Linear Regression.
Module 4: Logistic Regression.
Module 5: Support Vector Machines
Module 6: Hebbian Learning
Module 7: Expectation -Maximization algorithm
Module 8: McCulloch Pitts Model.
Module 9: Single Layer Perceptron Learning algorithm
Module 10: Error Backpropagation Perceptron Training Algorithm
Module 11: Principal Component Analysis
Module 12
- Applications of above algorithms as a case study (E.g. Hand Writing Recognition using MNIST data set, classification using IRIS data set, etc)
Useful Links
- 1 https://www.learndatasci.com/out/edx-columbia-machine-learning/
- 2 https://www.learndatasci.com/out/oreilly-hands-machine-learning-scikit-learn-keras-and-ten sorflow-2nd-edition/
- 3 https://www.learndatasci.com/out/google-machine-learning-crash-course/
Reproduced from the University of Mumbai syllabus for B.E. (Artificial Intelligence and Machine Learning) 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.