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 ( Training/Test/Evaluation)

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
-Lecture 5: Meaning Representations
-Lecture 6: Automatic Speech Recognition And Text-To-Speech Synthesis
-Lecture 7: NLU with Conditional Random Fields
-Lecture 8: NLU with Neural Networks
  • Lab 6    : RNN
-Lecture 9: Linguistics of Conversations
-Lecture 10: Confidence Measures
-Lecture 11: Dialogue Models & Evaluation
-Lecture 12: Deconstructing Bot Technology & Platforms