Description

The goal of this course is to study some advanced and current topics in machine learning. Different sources will be used, mainly advanced textbooks and research papers. The students read the material and work in the exercises.
This course corresponds to "Temas Avanzados en Sistemas Inteligentes I" 2019809

Room and Time

406-104 Wednesday 9:00am-1:00pm

Schedule

 Session         TopicExercises
Feb 26 Course description
Monte Carlo inference [Murphy2013] Chap 23

March 5 - 19Monte Carlo inference [Murphy2013] Chap 23
Sampling [Barber2013] Chap 27
[Murphy2013] 23.1, 23.2 and 23.3
[Barber2013] 27.3, 27.4, 27.6 and 27.7
March 26 - April 9Random forest [Hastie2009]  Chap 15[Hastie2009] 15.2, 15.3, 15.4, 15.5 and 15.6
April 23 - 30
Neural networks [Bishop2006] Chap 5[Bishop2006] 5.4, 5.5, 5.8, 5.12, 5.20, 5.27
May 21-28Continuous latent variables [Bishop2006] Chap 12
Unsupervised Linear Dimension Reduction [Barber2013] Chap 15
[Bishop2006] 12.2, 12.13, 12.19, 12.21, 12.26
[Barber2013] 15.4, 15.8
Junio 11-18Deep Learning  [Murphy2013] Sect 27.7, Chap 28 

Bibliography

 
[Barber2013] Barber, David, Bayesian Reasoning and Machine Learning, Cambridge University Press, 2013.

[Bishop2006] Bishop, C. Pattern Recognition and Machine Learning, Springer-Verlag, 2006.

[Hastie2009] Hastie, T. , Tibshirani, R. and Friedman. 2009, The elements of statistical learning: data mining, inference, and prediction, 2nd Ed, Springer-Verlag, 2009.

[Murphy2013] Murphy, Kevin P, Machine learning: a probabilistic perspective, The MIT Press, 2012.

Resources