Applied Machine Learning Assignments

Assignment 1:

In this assignment, we had to write a program to estimate the parameters of an unknown polynomial

Assignment 2:

In this assignment, we had to write a proram to find the coefficients for linear regression model by implementing the normal equation and batch and stochastic gradient descent.

Assignment 3:

In this assignment, we had to build a decision tree, use cross-validation to prune a tree, and evaluate the performance of the tree and interpret the results.

Assignment 4:

In this assignment, we had to build an NN on the titantic dataset and measure the performance of the model using in-sample and out-of-sample accuracy

Assignment 5:

In this assignment, we had to design a genetic algorithm to solve the polynomial fittnig problem in HW 1.

File Structure

.
├── HW1
│   └── HW1.ipynb
├── HW2
│   ├── Programming
│   │   ├── HW2\ -F20.pdf
│   │   ├── HW2.ipynb
│   │   ├── data2.txt
│   │   └── images
│   │       ├── firstHalf.png
│   │       ├── gradient_equation.png
│   │       ├── normalEquation.png
│   │       └── secondHalf.png
│   └── cquinto_hw2_CPE695.pdf
├── HW3
│   ├── A03.ipynb
│   ├── HW3-S20.pdf
│   ├── cquinto_hw3_CPE695.pdf
│   ├── data
│   │   └── Titanic.csv
│   └── images
│       ├── FullDecisionTree.png
│       └── PrunedDecisionTree.png
├── HW4
│   ├── cquinto_hw4.zip
│   ├── cquinto_hw4_CPE695.pdf
│   ├── data
│   │   └── Titanic.csv
│   └── main.ipynb
├── HW5
│   ├── cquinto_mutation_crossover.html
│   ├── cquinto_mutation_crossover.ipynb
│   ├── cquinto_mutation_only.html
│   └── cquinto_mutation_only.ipynb
└── README.md