Lecture 01

Computer Structure, Networks, and ML

Lecture 02

Data Visualization

Lecture 03

From Set Theory to Linear Regression

Lecture 04

From Linear to Logistic Regression

Lecture 05

From Logistic Regression to Multi-layer Perceptrons

Lecture 06

Supervised Learning Evaluation

Lecture 07

Data Modality and Feature Extraction

Lecture 08

Loss, Optimization, and Scheduling

Lecture 09

Initialization and Normalization

Lecture 10

Deep Learning

Lecture 11

Sequence Models

Lecture 12

Advanced Sequence Models

Lecture 13

Transformer Architecture

Lecture 14

Pre-trained Language Models and LLM Era

Lecture 15

Generative Models - GAN

Lecture 16

Generative Models - Diffusion

Lecture 17

Clustering and Unsupervised Learning

Lecture 18

Advanced Unsupervised Learning

Lecture 19

Model Explainability - XAI Fundamentals and Traditional Methods

Lecture 20

Model Explainability - SHAP and Deep Learning XAI