Language Understanding Systems ( 2017-2018)

Basic Machine Learning 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

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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