Machine Learning (Hung-yi Lee, NTU)
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What you'll learn
This course includes
- 28 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 36 lessons • 28 hours of video
Machine Learning (Hung-yi Lee, NTU)
36 lessons
• 28 hours
Machine Learning (Hung-yi Lee, NTU)
36 lessons
• 28 hours
- ML Lecture 0-1: Introduction of Machine Learning 38:57
- ML Lecture 0-2: Why we need to learn machine learning? 01:20
- ML Lecture 1: Regression - Case Study 01:18:35
- ML Lecture 1: Regression - Demo 06:53
- ML Lecture 2: Where does the error come from? 43:15
- ML Lecture 3-1: Gradient Descent 01:01:52
- ML Lecture 3-2: Gradient Descent (Demo by AOE) 02:35
- ML Lecture 3-3: Gradient Descent (Demo by Minecraft) 01:41
- ML Lecture 4: Classification 01:09:41
- ML Lecture 5: Logistic Regression 01:07:14
- ML Lecture 6: Brief Introduction of Deep Learning 46:31
- ML Lecture 7: Backpropagation 31:26
- ML Lecture 8-1: “Hello world” of deep learning 29:52
- ML Lecture 8-2: Keras 2.0 09:38
- ML Lecture 8-3: Keras Demo 11:13
- ML Lecture 9-1: Tips for Training DNN 01:26:03
- ML Lecture 9-2: Keras Demo 2 15:21
- ML Lecture 9-3: Fizz Buzz in Tensorflow (sequel) 06:10
- ML Lecture 10: Convolutional Neural Network 01:19:30
- ML Lecture 11: Why Deep? 57:45
- ML Lecture 12: Semi-supervised 01:00:00
- ML Lecture 13: Unsupervised Learning - Linear Methods 01:40:21
- ML Lecture 14: Unsupervised Learning - Word Embedding 40:39
- ML Lecture 15: Unsupervised Learning - Neighbor Embedding 30:59
- ML Lecture 16: Unsupervised Learning - Auto-encoder 42:05
- ML Lecture 17: Unsupervised Learning - Deep Generative Model (Part I) 29:35
- ML Lecture 18: Unsupervised Learning - Deep Generative Model (Part II) 01:03:31
- ML Lecture 19: Transfer Learning 01:14:29
- ML Lecture 20: Support Vector Machine (SVM) 01:05:28
- ML Lecture 21-1: Recurrent Neural Network (Part I) 49:00
- ML Lecture 21-2: Recurrent Neural Network (Part II) 01:30:51
- ML Lecture 22: Ensemble 01:39:59
- ML Lecture 23-1: Deep Reinforcement Learning 01:06:22
- ML Lecture 23-2: Policy Gradient (Supplementary Explanation) 13:21
- ML Lecture 23-3: Reinforcement Learning (including Q-learning) 01:05:34
- ML Lecture 21-1: Recurrent Neural Network (Part I) English version 46:24
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