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Hand selected research content, paper, class notes, projects etc material for Artificial intelligence and Deep Learning

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Hand selected research content, paper, class notes, projects etc material for Artificial intelligence and Deep Learning

Computer Vision

Fall 2018 Graduate course @ UB:

Lectures:

Projects

Project 1 : Edge, Keypoint, Cursor Detection
Project 2 : Image Features, Homography, Epipolar Geometry and Clustering

Extra Material


Biomedical Imaging Research Papers




NLP

Graduate Course Work Lectures

University Of Cambridge :

Introduction. Brief history of NLP research, current applications, components of NLP systems. Finite-state techniques. Inflectional and derivational morphology, finite-state automata in NLP, finite-state transducers. Prediction and part-of-speech tagging. Corpora, simple N-grams, word prediction, stochastic tagging, evaluating system performance. Context-free grammars and parsing. Generative grammar, context-free grammars, parsing with context-free grammars, weights and probabilities. Limitations of context-free grammars. Constraint-based grammars. Constraint-based grammar, unification.

Compositional semantics. Simple compositional semantics in constraint-based grammar. Compositional semantics with lambda calculus. Inference and robust entailment.

Lexical semantics. Semantic relations, WordNet, word senses, word sense disambiguation.

Distributional semantics Representing lexical meaning with distributions. Similarity metrics. Clustering.

Discourse and dialogue. Anaphora resolution, discourse relations.

Language generation Realization as inverse of parsing. N-grams and fluency. Text simplification.

Computational psycholinguistics: modelling human language use

Applications. Examples of practical applications of NLP techniques.


State University of New York

LexicalProcessing 1 : Lemmatization, Automata and Transducers | Chapter 2 & 3 : JM '09

LexicalProcessing 2 : Tokenization, Normalization, and Stemming | Chapter 2 : JM '09

Minimum Edit Distance | Chapter 3 : JM '09

Language Modelling (with n-grams) | Chapter 4 : JM '09

POS Tagging| Chapter 5 & 6 : JM '09 | Chapter 9 & 10 : MS

N-gram Modelling Continued | Chapter 4 : JM '09 | Chapter 6: MS

NLP with Neural Network : Part 1 | Chapter 9 : JM '17

Speech Recognition | Chapter 9 : JM ' 00

Formal Grammars | Chapter 12 : JM ' 00

Parsing 1 | Chapter 13 : JM '09

Parsing 2 | Chapter 13 : JM '09

Parsing 3 | Chapter 13 : JM '09

Representation of Meaning | Chapter 17 : JM '09

Computational Semantics 1 | Syntax-Semantics Interface | Chapter 18 : JM '09

Computational Semantics 2 | A Syntax-Semantics Interface in Prolog

Computational Semantics 3 | Questions and Gap Threading

Computational Semantics 4| Generalized quantifiers and scope ambiguity | Chapter 18 : JM '09

Computational Lexical Semantics | Chapter 19 : JM '09

Machine Translation | Chapter 25 : JM '25


Prolog

Lambda Calculus

First Order Language

Suggested readings


Experiments