MLDL 103 – Unsupervised Learning & Clustering

(Weeks 5–6 | Lecture 6 Hrs / Lab 18 Hrs / Ext 0 Hrs | 24 Total Contact Hrs | 1.0 Semester
Credit)
Students will:

• Apply KMeans, DBSCAN, and hierarchical clustering to find hidden patterns.
• Perform dimensionality reduction using PCA, t-SNE, and UMAP.
• Analyze and interpret clusters for applications like customer segmentation.
  Prerequisite: MLDL 102 – Supervised Learning Models
  Tools: Scikit-learn, Seaborn