Language Understanding Systems ( 2017-2018)

Machine Learning or equivalent course is a prerequisite.
In particular:

Mandatory Prerequisites
Basic Discrete Probability
Bayes’ Decision Theory
Generative Models
Naive Bayes’s Classifier
Maximum Likelihood Training
Supervised Learning Framework

Optional and Recommended
Discriminative Models
Logistic Regression


Links below require UNITN credentials

-Lecture 1: Intro to the Course

-Lecture 2: Sequence and Language Modeling

-Lecture 3: Weighted Finite State Transducers ( 1 , 2 )

-Lecture 4: NLU with Weighted Finite State Transducers

GUEST Lecture : “From Zero to Hero: The Road to Fully Automated Learning” , Dr. Giuseppe Di Fabbrizio
March 20, 2018

-Lecture 5: Meaning Representations

GUEST Lectures : “Neural Networks for Natural Language Understanding Systems” , Dr. Dilek Hakkani-Tur
April 9 and 10, 2018

-Lecture 6: NLU with Conditional Random Fields

-Lecture 7: NLU with Neural Networks
  • Lab 6    : RNN

-Lecture 8: Linguistics of Conversations

-Lecture 9: Dialogue Models & Evaluation

-Lecture 10: Automatic Speech Recognition and Confidence Measures

-Lecture 11: Deconstructing Bot Technology & Platforms