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!
LM101-026: How to Learn Statistical Regularities (Rerun)
LM101-025: How to Build a Lunar Lander Autopilot Learning Machine
LM101-024: How to Use Genetic Algorithms to Breed Learning Machines
LM101-023: How to Build a Deep Learning Machine
LM101-022: How to Learn to Solve Large Constraint Satisfaction Problems
LM101-021: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain)
LM101-020: How to Use Nonlinear Machine Learning Software to Make Predictions
LM101-019 (Rerun): How to Enhance Intelligence with a Robotic Body (Embodied Cognition)
LM101-018: Can Computers Think? A Mathematician's Response (Rerun)
LM101-017: How to Decide if a Machine is Artificially Intelligent (Rerun)
LM101-016: How to Analyze and Design Learning Rules using Gradient Descent Methods
LM101-015: How to Build a Machine that Can Learn Anything (The Perceptron)
LM101-014: How to Build a Machine that Can Do Anything (Function Approximation)
LM101-013: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)
LM101-012: How to Evaluate the Ability to Generalize from Experience (Cross-Validation Methods)
LM101-008: How to Represent Beliefs Using Probability Theory
LM101-011: How to Learn About Rare and Unseen Events (Smoothing Probabilistic Laws)
LM101-010: How to Learn Statistical Regularities (MAP and maximum likelihood estimation)
LM101-009: How to Enhance Intelligence with a Robotic Body (Embodied Cognition)
LM101-007: How to Reason About Uncertain Events using Fuzzy Set Theory and Fuzzy Measure Theory
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