LECTURE NOTES
MACHINE LEARNING
January 2018 -
Compilation of Lecture Notes from Duda & Hart's classic book. Link: https://drive.google.com/drive/folders/0B9K3zpr0Pox8UkNrS1RfX2lHUlk?usp=sharing
Link2: https://drive.google.com/drive/folders
/0B9K3zpr0Pox8Tm1UN0pJUVg1Zms?usp=sharing
Random Forests: https://drive.google.com/drive/folders/0B9K3zpr0Pox8VnNYejk2UndEczQ?usp=sharing
NUMERICAL METHODS
January 2023 - June 2025
Lecture notes and Ebook by one of the best authors for undergraduate audience.
Link: https://drive.google.com/drive/folders/0B9K3zpr0Pox8ZURxZWtQRkRiNUU?usp=sharing
LINEAR ALGEBRA
January 2018-
Link: https://drive.google.com/drive/folders/0B9K3zpr0Pox8UkNrS1RfX2lHUlk?usp=sharing
Lecture Video: https://www.youtube.com/watch?v=XnWhlPidyII
INFORMATION THEORY
Jan 2018-
I'm a paragraph. Click here to add your own text and edit me. It’s easy. Just click “Edit Text” or double click me to add your own content and make changes to the font. I’m a great place for you to tell a story and let your users know a little more about you.
PROBABILITY MODELS IN COMPUTER SCIENCE
January 2018 -
Compilation of Lecture Notes:
https://sites.google.com/a/pes.edu/probabilitymodels_computerscience/
Link2: https://drive.google.com/file/d/1crUQV4_fJnVznPmlFzlxKPJDcPfxSr77/view?usp=sharing
BUSINESS ANALYTICS
Jan 2018-
SVD-PCA-ICA
January 2018
SVD : # Dan Kalman
https://drive.google.com/drive/u/1/folders/14lkPxR_qfSZtNPF2cYtyjb0Szlzkj-WI
Lecture Notes and Codes from Dr. Nithin Nagraj:
https://drive.google.com/drive/folders/1QhlH6gf-lOi0wYpVspdDUv3Q2o7kIzNw?usp=sharing
EBOOK ON MACHINE LEARNING
January 2018 -
[work in progress] Snehanshu Saha, Kakoli Bora, Suryoday Basak, Gowri Srinivasa, Margarita Safonova, Jayant Murthy and Surbhi Agrawal, Machine learning in Astronomy: A Workman’s Manual
Read/download @
https://drive.google.com/drive/u/1/folders/1KH82zdKbFz7WnnIaSe9c1vZ5Efmi4qhg
[Python Codes in chapter 11]
NEURAL NET IMPLEMENTATION
January 2018-
https://github.com/mathurarchana77/neuralnetwork
The code implements back propagation algorithm with fuzzified inputs. It uses k-fold cross validation to enhance the accuracy of classification