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 .
LM101-046: How to Optimize Student Learning using Recurrent Neural Networks (Educational Technology)
LM101-045: How to Build a Deep Learning Machine for Answering Questions about Images
LM101-044: What happened at the Deep Reinforcement Learning Tutorial at the 2015 Neural Information Processing Systems Conference?
LM101-043: How to Learn a Monte Carlo Markov Chain to Solve Constraint Satisfaction Problems (Rerun of Episode 22)
LM101-042: What happened at the Monte Carlo Markov Chain (MCMC) Inference Methods Tutorial at the 2015 Neural Information Processing Systems Conference?
LM101-041: What happened at the 2015 Neural Information Processing Systems Deep Learning Tutorial?
LM101-040: How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms using Latent Semantic Analysis
LM101-039: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain and Markov Fields)[Rerun]
LM101-038: How to Model Knowledge Skill Growth Over Time using Bayesian Nets
LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory
LM101-036: How to Predict the Future from the Distant Past using Recurrent Neural Networks
LM101-035: What is a Neural Network and What is a Hot Dog?
LM101-034: How to Use Nonlinear Machine Learning Software to Make Predictions (Feedforward Perceptrons with Radial Basis Functions)[Rerun]
LM101-033: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)[RERUN]
LM101-032: How To Build a Support Vector Machine to Classify Patterns
LM101-031: How to Analyze and Design Learning Rules using Gradient Descent Methods (RERUN)
LM101-030: How to Improve Deep Learning Performance with Artificial Brain Damage (Dropout and Model Averaging)
LM101-029: How to Modernize Deep Learning with Rectilinear units, Convolutional Nets, and Max-Pooling
LM101-028: How to Evaluate the Ability to Generalize from Experience (Cross-Validation Methods)[RERUN]
LM101-027: How to Learn About Rare and Unseen Events (Smoothing Probabilistic Laws)[RERUN]
Create your
podcast in
minutes
It is Free
Insight Story: Tech Trends Unpacked
Zero-Shot
Fast Forward by Tomorrow Unlocked: Tech past, tech future
The Unbelivable Truth - Series 1 - 26 including specials and pilot
A Prairie Home Companion: News from Lake Wobegon