A collection of basic ML exercises (mostly based on Andrew Ng's ML course)

1. MLActivity 1: Univariate Linear Regression - Gradient Descent/Normal Equation (https://classroom.github.com/assignment-invitations/024acff4f1ded6c1ace98d3c578d3d53)
2. MLActivity 2: Multivariate Linear Regression (https://classroom.github.com/assignment-invitations/f290d82c45a72a473686f8f166771793)
3. MLActivity 3: Logistic Regression with Newton's Method (https://classroom.github.com/assignment-invitations/b426cca49bcafd4257f2ada95f9cb9f5)
4. MLActivity 4: Regularization for Linear and Logistic Regression (Titanic Shipwreck Dataset) (https://classroom.github.com/assignment-invitations/14b0e5bf6c491f41b5e8678ba19388ac)
5. MLActivity 5: Training vs. Testing for Linear and Logistic Regression (https://classroom.github.com/assignment-invitations/f4f7c42fd446aca358cf2d8924d974b3)
6. MLActivity 6: MNIST Binary Classification - 0 vs. 1 (https://classroom.github.com/assignment-invitations/a4f3b0eeda758842e7dddb7659b3607e)
7. MLActivity 7: MNIST Multi-class Classification - one vs. rest with Neural Network introduction (https://classroom.github.com/assignment-invitations/9aacf26fbad9d88c677584f323f0f41a)
8. MLActivity 8: Multi-class Classification and Neural Networks (https://classroom.github.com/assignment-invitations/fc2053ea96e77e8a5e45396de822c026)
9. MLActivity 9: Introduction to Neural Networks in iPython (https://github.com/DeLaSalleUniversity-Manila/Neural-Networks-Demystified)
10. MLActivity 10: XOR Example: Introduction to Fast Artificial Neural Network (FANN) Library (https://github.com/DeLaSalleUniversity-Manila/fann)
11. MLActivity 11: MNIST Classification with Fast Artificial Neural Network (FANN) Library (https://github.com/DeLaSalleUniversity-Manila/ArtificialNeuralNetworkWithFANNonMNIST)
12. MLActivity 12: Final Project Presentation

$ cd /path/to/your/files/
$ git init
$ git add –all
$ git commit -m "your message, e.x. Assignment 1 submission"
$ git remote add origin <Assignment link copied from assignment github, e.x. https://github.com/DeLaSalleUniversity-Manila/secondactivityassignment-melvincabatuan.git>
$ git push -u origin master
<then Enter Username and Password>

• Write an IEEE conference paper that applies an Artificial Neural Network (ANN) to solve a specific problem within the DLSU campus, or a unique Filipino problem.
• Proposal Pitch Video Deadline: November 5, 2015
• Paper Deadline: November 26, 2015

1. [REQUIRED] Dataset: Unique data from the Philippine setting or specific to DLSU ***
2. [REQUIRED] Divide data into Train, Validation, and Test
3. [REQUIRED] Cost function plot
4. [REQUIRED] MSE plots
5. [REQUIRED] Confusion matrix
6. [REQUIRED] Accuracy > 80 %

TO SUBMIT ALL CODES, DATASET, and IEEE Documentation, PLEASE CLICK THE FOLLOWING LINK:

https://classroom.github.com/assignment-invitations/156a06d938e7a332e2041c68499b8330

"Failure is success if we learn from it." - Malcolm Forbes