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Machine learning
Published on Oct 06, 20163213 Views
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Chapter list
MACHINE LEARNING00:00
Motivation/100:37
Motivation/201:02
Motivation/301:52
Outline03:43
What is Machine Learning/104:12
What is Machine Learning/205:27
Terminology06:00
DEMO SVM07:17
Standard Problems11:05
Supervised learning13:00
Classification/113:55
Classification/214:51
Classification/315:50
Document categorization16:56
Sentiment analysis17:19
Object detection17:48
Regression18:12
Estimate university GPA18:25
Probability of Default18:47
Ranking19:31
Search engine ranking20:06
Recommendation21:26
Unsupervised learning22:26
Clustering22:50
Clustering news articles24:24
Anomaly Detection25:33
Density-based Approaches26:20
Dimensionality Reduction27:27
Density Estimation28:20
Autoencoders28:42
Semi-supervised Learning/129:51
Semi-supervised Learning/231:03
Active Learning Example31:33
Data Representation34:41
Representing Text Documents/136:27
Representing Text Documents/239:36
Representing Text Documents/339:54
Learning Representation41:13
Time Series42:00
Machine Learning Models43:10
Linear Model44:20
Linear Model for Classification/145:57
Linear Model for Classification/246:14
Kernel Methods50:01
Popular Kernels: Polynomial Kernel50:10
Popular Kernels: Gaussian Kernel50:53
Artificial Neural Networks (ANN)51:12
TENSORFLOW53:06
Multilayer Perceptron01:05:23
NLP Tasks01:06:30
How to Fit a Model01:10:19
How to measure loss?01:11:04
Classification01:11:53
Maximum likelihood estimation01:13:10
How to ensure we generalize well?01:14:10
Ockham (Occam)’s Razor01:14:29
Model Overfitting01:14:48
Support Vector Machine/101:15:57
Support Vector Machine/201:16:46
Validation01:17:21
Q1: Quantifying Model Performance01:17:52
Binary Classification01:19:11
ROC Curve01:20:45
Area Under the Curve (AUC)01:21:31
Regression01:22:32
Q2: How well does it generalize?01:22:56
Confidenc01:24:51
Q3: Is it better then some other model?01:25:31
HANDS-ON 01:26:23