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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

Description:

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。

Machine Learning

National Taiwan University

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#Computer Science #Machine Learning #Deep Learning #Reinforcement Learning #Gradient Descent #Classification #Logistic Regression

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