Regularized linear regression model to predict the water flow from a dam | Examined effects of bias vs variance

Implementing regularised linear regression and using it to study models with different bias-variance properties. Insights for applying machine learning.

Here, we implement regularized linear regression to predict the amount of water flowing out of a dam using the change of water level in a reservoir. In the next half, we go through some diagnostics of debugging learning algorithms and examine the effects of bias v.s. variance.

Machine Learning Exercises from Online Course (Coursera)

Machine learning techniques, such as Linear Regression, Logistic Regression, Neural Networks (feedforward propagation, backpropagation algorithms), Diagnosing Bias/Variance, Evaluating a Hypothesis, Learning Curves, Error Analysis, Support Vector Machines, K-Means Clustering, PCA, Anomaly Detection System, and Recommender System.

My lecture notes and assignment solutions for the Coursera machine learning class taught by Andrew Ng.