hsmmi

bayesian

Homework#3 in Statistical Pattern Recognition at Shiraz University

Classification-with-Single-Layer-Neural-Networks

1st homework of Neural Networks and Deep Learning at Shiraz University

clustering-equivalence-relation

Project#1 on Fuzzy system at Shiraz University

DesicionTree

It's 4th homework of Machine Learning in Shiraz university

digikala-bootcamp

Summer 2022 - Software Engneering - Digikala

digikala-bootcamp-day2

linear-regression

Linear regression tries to model the relationship between two variables by applying a linear equation to a series of data. Your task is to train linear model on the given datasets. Implement linear regression using closed form solution and the Gradient Descent algorithm (Batch or Stochastic, only one) and test your implementation on the given Test Dataset. • Use the Data-Train to train your model, and test on the Data-Test. • Plot of the datasets and regression lines. • Plot cost function for enough iteration for linear regression in Gradient Descent (Batch or stochastic, only one). • Report the learned parameters (θ0 , θ1 , ..., θn ), and also the value of MSE error on the train and test data. • Do not forget that you could normalize the data.

logistic_and_softmax_regression

Homework#2 in Statistical Pattern Recognition at Shiraz University

PStability-Prototype-Selection

Implementation on my thesis

Word-Document-Citation-Tools

hsmmi/bayesian

Homework#3 in Statistical Pattern Recognition at Shiraz University

hsmmi/linear-regression

Linear regression tries to model the relationship between two variables by applying a linear equation to a series of data. Your task is to train linear model on the given datasets. Implement linear regression using closed form solution and the Gradient Descent algorithm (Batch or Stochastic, only one) and test your implementation on the given Test Dataset. • Use the Data-Train to train your model, and test on the Data-Test. • Plot of the datasets and regression lines. • Plot cost function for enough iteration for linear regression in Gradient Descent (Batch or stochastic, only one). • Report the learned parameters (θ0 , θ1 , ..., θn ), and also the value of MSE error on the train and test data. • Do not forget that you could normalize the data.

hsmmi/logistic_and_softmax_regression

Homework#2 in Statistical Pattern Recognition at Shiraz University

hsmmi/clustering-equivalence-relation

Project#1 on Fuzzy system at Shiraz University

hsmmi/fuzzy-c-mean

It's project#3 in Fuzzy system course at Shiraz University

hsmmi/PCA-Fisher-LDA

Part of Homework#4 in Statistical Pattern Recognition at Shiraz University