This particular podcast covers the material in Chapter 3 of my new book “Statistical Machine Learning: A unified framework” with expected publication date May 2020. In this episode we discuss Chapter 3 of my new book which discusses how to formally define machine learning algorithms. Briefly, a learning machine is viewed as a dynamical system that is minimizing an objective function. In addition, the knowledge structure of the learning machine is interpreted as a preference relation graph which is implicitly specified by the objective function. In addition, this week we include in our book review section a new book titled “The Practioner’s Guide to Graph Data” by Denise Gosnell and Matthias Broecheler. To find out more information visit the website: www.learningmachines101.com .
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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