Machine Learning Unit 5.1 Semester 3 MSc IT Part 2 Mumbai University
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Probabilistic Model: Normal Distribution and Its Geometric Interpretations, Na誰ve Bayes Classifier, Discriminative learning with Maximum likelihood, Probabilistic Models with Hidden variables: Estimation - Maximization Methods, Gaussian Mixtures, and Compression based Models.
Machine Learning Unit 5.1 Semester 3 MSc IT Part 2 Mumbai University
- 1. General Unit 5 ML Idol MU Machine Learning PPT BY MADHAV MISHRA Part 1
- 2. General Unit 5 ML Idol MU Table Of Contents PPT BY MADHAV MISHRA 2
- 3. General Normal Distribution and Its Geometric Interpretations PPT BY MADHAV MISHRA 3
- 4. General PPT BY MADHAV MISHRA 4
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- 7. General PPT BY MADHAV MISHRA 7
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- 10. General Na誰ve Bayes Classifier PPT BY MADHAV MISHRA 10
- 11. General PPT BY MADHAV MISHRA 11
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- 14. General PPT BY MADHAV MISHRA 14
- 15. General Discriminative learning with Maximum likelihood PPT BY MADHAV MISHRA 15
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- 24. General PPT BY MADHAV MISHRA 24
- 25. General Compression based Models PPT BY MADHAV MISHRA 25 Important Links: https://towardsdatascience.com/three-model-compression-methods-you-need-to-know-in-2021- 1adee49cc35a https://xailient.com/blog/4-popular-model-compression-techniques-explained/