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The projects are part of the graduate-level course CSE-574 : Introduction to Machine Learning [Spring 2019 @ UB_SUNY] . . . Course Instructor : Mingchen Gao (https://cse.buffalo.edu/~mgao8/)

Python 14.31% Jupyter Notebook 85.69%
machine-learning classification-algorithm regression-algorithms linear-discriminant-analysis-lda quadratic-discriminant-analysis lda qda linear-regression ridge-regression non-linear-regression

ml_projects's Introduction

Spring 2019

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CSE-574: Intro Machine Learning

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Problem: The project is about different classification and regression techniques. There are six sub-problems under this project.

  • Experiment with Gaussian Discriminators by implementing Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA)
  • Linear Regression implementation
  • Experiment with Ridge Regression
  • Using Gradient Descent for Ridge Regression
  • Non-linear Regression
  • Compare and interpret the results of different classification and regression methods

                                                                                      Code        Report

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Problem: Handwritten Digits Classification using Multilayer Perceptron (MLP) Neural Network

Approach:

  • Implemented MLP Neural Network from scratch to classify handwritten digits from MNIST dataset (achieved test accuracy of 93.45%)
  • Used Feed Forward and Back Propagation to implement Neural Network
  • Experimented the effect of regularization in the bias-variance trade-off
  • Used the same neural network for more challenging face dataset and compared it with Deep Neural Network and Convolutional Neural Network using the Tensorflow library

                                                                                      Code        Report

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Problem: The project is about Support Vector Machine (SVM) and mulit-class logistic regression.

Approach:

  • Implemented Logistic Regression (using one-vs-all strategy) to classify handwritten digit images into correct corresponding labels. In particular, built 10 binary-classifiers (one for each class) to distinguish a given class from all other classes.
  • Developed multiclass logistic regression classifier and SVM for handwritten digits classification on the MNIST Dataset

                                                                                      Code        Report


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