探索機器學習的全面課程，從基礎概念到高級技術。學習回歸、梯度下降、分類、邏輯回歸和深度學習的基本原理。掌握卷積神經網絡、半監督和無監督學習方法，包括線性方法、詞嵌入和自編碼器。深入研究深度生成模型、遷移學習、支持向量機和循環神經網絡。探索集成學習和深度強化學習的原理。通過實際演示和案例研究，獲得使用Keras和TensorFlow等流行工具的實踐經驗。適合希望全面了解現代機器學習技術的學習者。
Overview
Syllabus
ML Lecture 0-1: Introduction of Machine Learning.
ML Lecture 0-2: Why we need to learn machine learning?.
ML Lecture 1: Regression - Case Study.
ML Lecture 1: Regression - Demo.
ML Lecture 2: Where does the error come from?.
ML Lecture 3-1: Gradient Descent.
ML Lecture 3-2: Gradient Descent (Demo by AOE).
ML Lecture 3-3: Gradient Descent (Demo by Minecraft).
ML Lecture 4: Classification.
ML Lecture 5: Logistic Regression.
ML Lecture 6: Brief Introduction of Deep Learning.
ML Lecture 7: Backpropagation.
ML Lecture 8-1: “Hello world” of deep learning.
ML Lecture 8-2: Keras 2.0.
ML Lecture 8-3: Keras Demo.
ML Lecture 9-1: Tips for Training DNN.
ML Lecture 9-2: Keras Demo 2.
ML Lecture 9-3: Fizz Buzz in Tensorflow (sequel).
ML Lecture 10: Convolutional Neural Network.
ML Lecture 11: Why Deep?.
ML Lecture 12: Semi-supervised.
ML Lecture 13: Unsupervised Learning - Linear Methods.
ML Lecture 14: Unsupervised Learning - Word Embedding.
ML Lecture 15: Unsupervised Learning - Neighbor Embedding.
ML Lecture 16: Unsupervised Learning - Auto-encoder.
ML Lecture 17: Unsupervised Learning - Deep Generative Model (Part I).
ML Lecture 18: Unsupervised Learning - Deep Generative Model (Part II).
ML Lecture 19: Transfer Learning.
ML Lecture 20: Support Vector Machine (SVM).
ML Lecture 21-1: Recurrent Neural Network (Part I).
ML Lecture 21-2: Recurrent Neural Network (Part II).
ML Lecture 22: Ensemble.
ML Lecture 23-1: Deep Reinforcement Learning.
ML Lecture 23-2: Policy Gradient (Supplementary Explanation).
ML Lecture 23-3: Reinforcement Learning (including Q-learning).
ML Lecture 21-1: Recurrent Neural Network (Part I) English version.
Taught by
Hung-yi Lee
