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LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding
Manage episode 230297532 series 2497400
This 70th episode of Learning Machines 101 we discuss how to identify facial emotion expressions in images using an advanced clustering technique called Stochastic Neighborhood Embedding. We discuss the concept of recognizing facial emotions in images including applications to problems such as: improving online communication quality, identifying suspicious individuals such as terrorists using video cameras, improving lie detector tests, improving athletic performance by providing emotion feedback, and designing smart advertising which can look at the customer’s face to determine if they are bored or interested and dynamically adapt the advertising accordingly. To address this problem we review clustering algorithm methods including K-means clustering, Linear Discriminant Analysis, Spectral Clustering, and the relatively new technique of Stochastic Neighborhood Embedding (SNE) clustering. At the end of this podcast we provide a brief review of the classic machine learning text by Christopher Bishop titled “Pattern Recognition and Machine Learning”.
Make sure to visit: www.learningmachines101.com to obtain free transcripts of this podcast and important supplemental reference materials!
85 episodes
Manage episode 230297532 series 2497400
This 70th episode of Learning Machines 101 we discuss how to identify facial emotion expressions in images using an advanced clustering technique called Stochastic Neighborhood Embedding. We discuss the concept of recognizing facial emotions in images including applications to problems such as: improving online communication quality, identifying suspicious individuals such as terrorists using video cameras, improving lie detector tests, improving athletic performance by providing emotion feedback, and designing smart advertising which can look at the customer’s face to determine if they are bored or interested and dynamically adapt the advertising accordingly. To address this problem we review clustering algorithm methods including K-means clustering, Linear Discriminant Analysis, Spectral Clustering, and the relatively new technique of Stochastic Neighborhood Embedding (SNE) clustering. At the end of this podcast we provide a brief review of the classic machine learning text by Christopher Bishop titled “Pattern Recognition and Machine Learning”.
Make sure to visit: www.learningmachines101.com to obtain free transcripts of this podcast and important supplemental reference materials!
85 episodes
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1 LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems 33:13


1 LM101-083: Ch5: How to Use Calculus to Design Learning Machines 34:22


1 LM101-082: Ch4: How to Analyze and Design Linear Machines 29:05


1 LM101-081: Ch3: How to Define Machine Learning (or at Least Try) 37:20


1 LM101-080: Ch2: How to Represent Knowledge using Set Theory 31:43


1 LM101-079: Ch1: How to View Learning as Risk Minimization 26:07


1 LM101-078: Ch0: How to Become a Machine Learning Expert 39:18


1 LM101-077: How to Choose the Best Model using BIC 24:15


1 LM101-076: How to Choose the Best Model using AIC and GAIC 28:17


1 LM101-075: Can computers think? A Mathematician's Response (remix) 36:26


1 LM101-074: How to Represent Knowledge using Logical Rules (remix) 19:22


1 LM101-073: How to Build a Machine that Learns to Play Checkers (remix) 24:58


1 LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (Remix of LM101-001 and LM101-002) 22:07
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