Learning

# What could go wrong? – Heteroskedasticity – Multicollinearity – Latent variables # Usin

Model: $Y = \theta_0^* + \theta_1^* X_1 + \cdots + \theta_m^* X_m + \text{noise}$ We obtain estimate, $\hat

Key Concepts Distribution of estimator hat \hat{\theta} Standard error of an estimator and confidence inter

Notation Key Vectors: boldface Scalars: normal font X₂: second data record X₂: second component of dataset

Linear Regression Overview Main Objective Predict the value of an unobserved variable y based on knowledge

Learning Topics Linear regression Central methods in ML The concept of classification problem through graph

Partitioning Around Medoids (PAM) K-Means Characteristics Cluster centers can be arbitrary points in space

K-means Clustering Overview K-means clustering is an unsupervised learning algorithm. It tries to aggregate

Clustering in Network Analysis Finding Groups in Network Communities When given a dataset, finding groups i

Overview Unsupervised learning algorithms use different distance or similarity/dissimilarity measures betwe