機器學習 Machine Learning--李育杰 / 交大

source: NCTU OCW      2017年9月20日
"Google's always used machine learning. In all the areas we applied it to, speech recognition, then image understanding, and eventually language understanding, we saw tremendous improvements."
John Giannandrea, then VP of Engineering, Google
In the last decade, machine learning has been applied to many real world problems successfully. It is considered as the most essential and fundmental knowledge for a data scientist. We introduce core concept of machine learning and several useful learning methods including linear models, nonlinear models, kernel methods, dimension reduction, unsupervised learning (Clustering) and deep learning. Also some special topics and applications will be discussed.
授課教師：應用數學系 李育杰老師
課程資訊：http://ocw.nctu.edu.tw/course_detail....
更多課程歡迎瀏覽交大開放式課程網站：http://ocw.nctu.edu.tw/

1 29:30 Introduction to Machine Learning
2 41:17 The Growth of a Data Scientist
3 35:58 Machine Learning: Overview (1/2)
4 18:34 Machine Learning: Overview (2/2)
5 52:44 Mathematical Background (1/2)
6 36:26 Mathematical Background (2/2)
7 45:55 Three Fundamental Learning Algorithms - Naive Bayes Algorithm
8 38:37 Three Fundamental Learning Algorithms - k-Nearest Neighbor Algorithm
9 35:04 Three Fundamental Learning Algorithms - Perceptron Algorithm (1/2)
10 37:04 Three Fundamental Learning Algorithms - Perceptron Algorithm (2/2)
11 1:08:43 Evaluating the Learning Models
12 41:38 Learning Theory (1/2)
13 39:45 Learning Theory (2/2)
14 40:46 Optimization (1/5)
15 41:34 Optimization (2/5)
16 1:08:43 Optimization (3/5)
17 42:59 Optimization (4/5)
18 45:24 Optimization (5/5)
19 33:23 Support Vector Machine(SVM) (1/6)
20 44:16 Support Vector Machine(SVM) (2/6)
21 1:17:32 Support Vector Machine(SVM) (3/6)
22 43:26 Support Vector Machine(SVM) (4/6)
23 46:15 Support Vector Machine(SVM) (5/6)
24 32:42 Support Vector Machine(SVM) (6/6)
25 39:03 Sequential Minimal Optimization (SMO) (1/2)
26 29:22 Sequential Minimal Optimization (SMO) (2/2)
27 47:22 Anomaly Detection via Online Over Sampling Principal Component Analysis (1/2)
28 55:12 Anomaly Detection via Online Over Sampling Principal Component Analysis (2/2)
29 34:20 Nonlinear Dimension Reduction with Kernel Sliced Inverse Regression
30 53:46 Clustering and EM Algorithm (1/4)
31 25:58 Clustering and EM Algorithm (2/4)
32 40:23 Clustering and EM Algorithm (3/4)
33 22:04 Clustering and EM Algorithm (4/4)
34 45:47 Online Nonlinear Support Vector Machine for Large-Scale Classification (1/2)
35 50:16 Online Nonlinear Support Vector Machine for Large-Scale Classification (2/2)