ปัญญาประดิษฐ์

Introduction to AI & Responsible AI

AI History & Types; Ethics และ Bias; Cloud Tools Setup

Search & Pathfinding in AI

State Space Search; BFS, DFS, A* Algorithms; Heuristics

ML Foundations + Basic Model Interpretability

Supervised/Unsupervised Learning; Model Evaluation Metrics; Decision Tree Visualization

Feature Engineering + Feature Importance

Feature Selection; Feature Creation; Pipeline Design; SHAP Values (เบื้องต้น)

Neural Network Fundamentals

Perceptron → MLP; Backpropagation Algorithm; Activation Functions

Deep Learning & CNN + CNN Visualization

CNN Architecture; Convolution & Pooling Layers; Feature Maps Visualization; Grad-CAM

Learning Optimization & Midterm Review

Learning Rate Scheduling; Optimizers (Adam, SGD, RMSprop); Review Sessions

สอบกลางภาค (Midterm Exam)

Integrated Skills Assessment

XAI Deep Dive + Trustworthy Models

SHAP & LIME (แบบละเอียด); Regularization (L1/L2, Dropout); Bias Detection; Model Cards

NLP & Transformer Architecture

Tokenization & Embeddings; Attention Mechanism; BERT Architecture; Thai NLP Challenges

Transfer Learning & Foundation Models

Fine-tuning Strategies; Feature Extraction; Data Collection Best Practices; Model Selection

Model Deployment & MLOps + XAI in Production

API Development (FastAPI); Model Versioning; Containerization; Explanation APIs

Large Language Models & RAG

Prompt Engineering; Vector Databases; RAG Architecture; Context Management

Portfolio Preparation & Project Work

Final Project Development; Documentation; Testing & Debugging

Project Showcase & Submission

Project Presentations; Peer Review; Final Submission

สอบปลายภาค (Final Exam)

Comprehensive Assessment