Assignments submitted as a part of Coursera machine learning course July 2014.
Majority of code is written in Octave 3.8.2 and some in 3.8.1.
Following are the topics covered-
a. Univariate linear regression
b. Multivariate linear regression
c. Gradient Descent
d. Normal Equation
a. Sigmoid Function
b. Regularized logistic regression
c. Vectorized cost function
a. Vectorized logistic regression and gradient descent
b. One-vs-All Prediction
c. Feed forward propagation
d. Backpropagation
e. Regularized neural networks
a. Learning curves
b. Polynomial regression
a. Gaussian kernel
b. Email Classification
a. Loyds algorithm of K means
b. Image Compression using Kmeans
c. PCA implementation using SVD
d. Reconstructing approximate representation of data using reduced dimension
a. Selecting threshold for Gaussian Distribution
b. Preecision and Recall
c. Movie Rating System- COllaborative filtering
- Download this repository by
git clone https://github.com/hrushikesh-dhumal/Coursera_Machine_Learning.git
- Download and install Octave
In each of the exercise folder there is a pdf file with problem description and it tells which file to execute. For Linear regression execute the ex1 from Octave.
"We strongly encourage students to form study groups, and discuss the lecture videos (including in-video questions). We also encourage you to get together with friends to watch the videos together as a group. However, the answers that you submit for the review questions should be your own work. For the programming exercises, you are welcome to discuss them with other students, discuss specific algorithms, properties of algorithms, etc.; we ask only that you not look at any source code written by a different student, nor show your solution code to other students."
Hrushikesh Dhumal ([email protected])
MIT