Study Note

This page is my archive of study notes across Generative AI, Machine Learning / Deep Learning, Optimization, Statistics, and Others (useful fundamentals that support my research). To view articles for a specific area only, use the Categories filter on the right.

Generative AI_2

Conditional Diffusion Model with expansion to the Time-series data application

Generative AI

This tutorial provides a structured and practical explanation of conditional diffusion models, building directly on the fundamentals of DDPMs and extending them to…

Generative AI_1

Fundamentals of Diffusion Model: Denoising Diffusion Probabilistic Models (DDPM)

Generative AI

This lecture note explains the fundamentals of Denoising Diffusion Probabilistic Models (DDPMs) as a modern generative modeling framework. It begins with the forward…

Scientific Machine Learning 16

Gaussian Process

Machine Learning

This lecture note introduces Gaussian Processes (GPs) as probabilistic models for regression that provide both predictions and calibrated uncertainty. It begins with a…

Scientific Machine Learning 15

Dimensionality Reduction with Principal Component Analysis (PCA)

Machine Learning

This lecture note explains Principal Component Analysis (PCA) as a linear dimensionality reduction method that removes redundancy and noise by rotating the coordinate system…

Scientific Machine Learning 14

All about Support Vector Machines

Machine Learning

This lecture note explains Support Vector Machines (SVMs) as maximum-margin classifiers that select the separating hyperplane which maximizes the distance to the nearest…

Scientific Machine Learning 13

Ensemble Learning: Bagging and Boosting

Machine Learning

This lecture note explains ensemble learning as a systematic way to improve prediction accuracy by combining multiple base models, starting from the bias–variance…

Scientific Machine Learning 12

Machine Learning

This lecture note explains decision trees as intuitive, non-parametric models that make predictions through a sequence of simple if–then rules learned directly from data. It…

Scientific Machine Learning 11

Naïve Bayes Classifier

Machine Learning

This lecture note introduces the Naïve Bayes classifier as a probabilistic, parametric classification model derived directly from Bayes’ theorem. It begins by defining…

Scientific Machine Learning 10

K-Nearest Neighbor and Feature Scaling

Machine Learning

This lecture note introduces K-Nearest Neighbors (KNN) as a simple, non-parametric, and instance-based learning algorithm in which the training data itself serves as the…

Scientific Machine Learning 09

Logistic Regression

Machine Learning

This lecture note explains logistic regression as a probabilistic classification model and clarifies why plain linear regression fails for classification tasks. It begins by…

Scientific Machine Learning 08

Generalized Linear Regression, Subset Selection, and Shrinkage

Machine Learning

This lecture note builds linear regression from first principles and explains why it remains a core foundation for modern machine learning. It starts with simple and…

Scientific Machine Learning 04

Cross-Entropy, Softmax, and Negative Log-Likelihood

Deep Learning

This lecture note explains why the common pairing of a sigmoid activation with a quadratic (MSE) loss often leads to very slow learning, and how better loss–activation…

Scientific Machine Learning 03

All about Backpropagation in DL

Deep Learning

This lecture note explains backpropagation as the core algorithm that enables neural networks to compute gradients efficiently and learn from data. It begins by introducing…

Scientific Machine Learning 02

Classifying Handwritten Digits & All about Gradient Descent

Deep Learning

This lecture note explains how the basic neural network concepts from the previous chapter are used to build and train a real classifier, using handwritten digit recognition…

Scientific Machine Learning 01

Perceptrons, Sigmoid Neurons, and Artificial Neural Networks

Deep Learning

This lecture note introduces the most fundamental building blocks required to study deep learning: perceptrons, sigmoid neurons, and artificial neural networks. It begins…