B.E. (Artificial Intelligence and Data Science) Machine Learning Lab Syllabus - Mumbai University
This is the TY BE AI and DS syllabus under CBCS REV-2019 'C' Scheme, in force from the academic year 2022-23. The University has published no NEP 2020 syllabus for Semesters V to VIII of any engineering branch, so this is the scheme you are examined on — exam form 1T01815 and 1T01816. The first and second years of the degree are on NEP 2020.
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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/
- 4 https://www.learndatasci.com/out/edx-columbia-machine-learning/
Reproduced from the University of Mumbai syllabus for B.E. (Artificial Intelligence and Data Science), item 6.42 (R), under CBCS REV-2019 'C' Scheme, in force from the academic year 2022-23. Wording, module numbering and hours are as printed in that syllabus.
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.