🤖 機器學習
從基礎理論到實務應用，探索機器學習的核心概念與演算法
📖 課程筆記
Parametric Methods
用固定數量的參數來描述機率分佈，透過 MLE 或 MAP 從資料中學習參數值
Ch03: Bayesian Decision Theory
Bayesian 分類、Loss 與 Risk、Discriminant Functions、Decision Boundaries
Ch02: Supervised Learning
監督式學習框架、Hypothesis Space、VC Dimension、PAC Learning 理論
Ch01: Introduction to Machine Learning
機器學習簡介：定義、主要類型、核心議題與 Bayes' Rule
📚 主題架構
監督式學習 (Supervised Learning)
- Linear Regression
- Logistic Regression
- Decision Trees
- Support Vector Machines
- Neural Networks
非監督式學習 (Unsupervised Learning)
- K-Means Clustering
- PCA (Principal Component Analysis)
- Autoencoders
深度學習 (Deep Learning)
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Transformers
- Attention Mechanism
理論基礎 (Theory)
- Bias-Variance Tradeoff
- Regularization
- Gradient Descent
- Backpropagation