LM101-081: Ch3: How to Define Machine Learning (or at Least Try) 64354o

Learning Machines 101

09/04/2020

This particular podcast covers the material in Chapter 3 of my new book “Statistical Machine...

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 .

LM1010-082: Ch4: How to Analyze and Design Linear Machines +1 año 29:04 LM101-083: Ch5: How to Use Calculus to Design Learning Machines +1 año 34:21 LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems +1 año 33:12 LM101-085:Ch7:How to Guarantee your Batch Learning Algorithm Converges +1 año 30:50 LM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes +1 año 35:28 Ver más en APP Comentarios del episodio 4a3h14