Interactive Portfolio
Machine Learning & Computer Vision
Interactive study guides — built for understanding, not reading.
Machine Learning
Sessions 15–22 · BINUS University
Session 15–16
Decision Trees & Ensembles
Decision trees, bagging, boosting, random forests, stacking.
Session 17–18
Support Vector Machines
Maximal margin classifier, kernel trick, RBF and polynomial kernels.
Session 19–20
Clustering
K-means, hierarchical clustering, DBSCAN.
Session 21–22
Dimensionality Reduction
PCA, LDA, t-SNE — comparing linear and nonlinear methods.
Computer Vision
Interactive Lab · Vol I
Vol I · Part 01
Image Filtering
Spatial convolution, kernel design, edge detection.
Vol I · Part 02
Feature Detection
Harris corners, ORB matching, homography estimation.
Vol I · Part 03
Camera Geometry
Pinhole model, intrinsic and extrinsic parameters.
Vol I · Part 04
Epipolar Geometry
Fundamental matrix, epipolar lines, triangulation.