Ciencias de la Computación FAMAF

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====== Teóricos UBA 2019 ======

[[materias:pln:uba2019|(volver a la página principal)]]

Recursos principales:

  * Natural Language Processing (Dan Jurafsky & Christopher Manning, Stanford, 2014)
    * **[[http://web.stanford.edu/~jurafsky/NLPCourseraSlides.html|Lecture Slides]]**
    * **[[https://www.youtube.com/playlist?list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm|Video Lectures]]**
    * **[[http://academictorrents.com/details/d2c8f8f1651740520b7dfab23438d89bc8c0c0ab|Torrent]]**
  * [[http://web.stanford.edu/class/cs224n/|Natural Language Processing with Deep Learning (Chris Manning, Stanford, 2019)]] ([[https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1184/|Winter 2018]], [[https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1174/|Winter 2017]])
    * **[[https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6|Video Lectures (2017)]]**
    * **[[https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z|Video Lectures (2019)]]**
  * [[https://www.deeplearning.ai/machine-learning-yearning/|Andrew Ng. “Machine Learning Yearning”. Draft, 2018.]]


===== 1ra clase =====

  * [[https://docs.google.com/presentation/d/e/2PACX-1vSbw7apSxJP9aDHAhJbBEUy2lWxrYRsgvX1bABrJGTPr7dzqLO4RVcODql1M0gj1fp-GkgCHPvbKXZp/pub?start=false&loop=false&delayms=3000|Presentación de la materia]]
  * [[https://www.youtube.com/watch?v=3Dt_yh1mf_U&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm&index=1|Introducción al Procesamiento de Lenguaje Natural]]
  * [[https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1174/lectures/cs224n-2017-lecture1.pdf|Introducción al PLN con Deep Learning]] (1ras 17 slides)
  * Procesamiento básico de texto:
    * [[https://www.youtube.com/watch?v=zfH2ADGtzJQ&index=2&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm|Expresiones regulares]]
    * [[https://www.youtube.com/watch?v=k242_PpMEsQ&index=3&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm|Expresiones regulares aplicadas al PLN]]
    * [[https://www.youtube.com/watch?v=f9o514a-kuc&index=4&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm|Tokenización de palabras]]
    * [[https://www.youtube.com/watch?v=ZhyLgPnOeh0&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm&index=5|Normalización, lematización y stemming]]
    * [[https://www.youtube.com/watch?v=UL4Ez56AMVo&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm&index=6|Segmentación de oraciones]]


  * Modelado de Lenguaje:
    * [[https://www.youtube.com/watch?v=Saq1QagC8KY&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm&index=12|Introducción a n-gramas]]
    * [[https://www.youtube.com/watch?v=paCMAZ-lKq8&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm&index=13|Estimando probabilidades de n-gramas]]
    * [[https://www.youtube.com/watch?v=b6nwdc_fGfA&index=14&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm|Evaluación y perplejidad]]
    * [[https://www.youtube.com/watch?v=6NeUDr7YDiw&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm&index=15|Generalización y ceros]]
    * [[https://www.youtube.com/watch?v=ZbHFLgBWgdQ&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm&index=16|Suavizado "add one"]]
    * [[https://www.youtube.com/watch?v=naNezonMA7k&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm&index=17|Interpolación]]

  * Notebooks:
    * [[https://github.com/PLN-FaMAF/pln-uba-2019/blob/master/notebooks/01%20Procesamiento%20B%C3%A1sico%20de%20Texto.ipynb|01 Procesamiento Básico de Texto]]
    * [[https://github.com/PLN-FaMAF/pln-uba-2019/blob/master/notebooks/02%20Modelado%20de%20Lenguaje.ipynb|02 Modelado de Lenguaje]]

  * Material complementario:
    * {{ :materias:pln:2019:lm-spring2013.pdf |Language Modeling
(Course notes for NLP by Michael Collins, Columbia University)}}
    * {{ :materias:pln:2019:lm-notas.pdf |Modelado de Lenguaje: Notas Complementarias}}


===== 2da clase =====


  * Etiquetado de secuencias:
    * [[https://www.youtube.com/watch?v=JhJU0Akkqzo&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm&index=56|Introducción al Part-of-Speech (POS) tagging]]
    * [[https://www.youtube.com/watch?v=Zm_bmRhbaQg&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm&index=57|Algunos métodos y resultados]]
  * Más sobre etiquetado de secuencias (curso de Collins):
    * [[https://www.youtube.com/watch?v=6jUva-eD-xY&list=PLlQBy7xY8mbI13gwXZz4r55MeatSZOqm7&index=1|Etiquetado de secuencias]]
    * [[https://www.youtube.com/watch?v=VMZM7AYjEsg&list=PLlQBy7xY8mbI13gwXZz4r55MeatSZOqm7&index=2|Modelos generativos para el aprendizaje supervisado]]
    * [[https://www.youtube.com/watch?v=cAJmM5k62yM&list=PLlQBy7xY8mbI13gwXZz4r55MeatSZOqm7&index=3|Introducción a los Hidden Markov Models (HMMs)]]
    * [[https://www.youtube.com/watch?v=uAT3iJpQwJ0&list=PLlQBy7xY8mbI13gwXZz4r55MeatSZOqm7&index=4|Estimación de parámetros de HMMs]]
    * [[https://www.youtube.com/watch?v=ECu_KQV3V30&list=PLlQBy7xY8mbI13gwXZz4r55MeatSZOqm7&index=5|5. The Viterbi Algorithm for HMMs - Part I]]
    * [[https://www.youtube.com/watch?v=WqGUa54x8wE&list=PLlQBy7xY8mbI13gwXZz4r55MeatSZOqm7&index=6|6. The Viterbi Algorithm for HMMs - Part II]]
    * [[https://www.youtube.com/watch?v=Bu7oSlNCmdU&list=PLlQBy7xY8mbI13gwXZz4r55MeatSZOqm7&index=7|7. The Viterbi Algorithm for HMMs - Part III]]
    * [[https://www.youtube.com/watch?v=Y5hXE23Tdzc&list=PLlQBy7xY8mbI13gwXZz4r55MeatSZOqm7&index=8|8. Summary]]


  * Notebooks:
    * [[https://github.com/PLN-FaMAF/pln-uba-2019/blob/master/notebooks/tagging/01%20Etiquetado%20de%20Secuencias%20Parte%201.ipynb|01 Etiquetado de Secuencias Parte 1]]
    * [[https://github.com/PLN-FaMAF/pln-uba-2019/blob/master/notebooks/tagging/02%20Etiquetado%20de%20Secuencias%20Parte%202.ipynb|02 Etiquetado de Secuencias Parte 2]]
    * [[https://github.com/PLN-FaMAF/pln-uba-2019/blob/master/notebooks/tagging/03%20Etiquetado%20de%20Secuencias%20Parte%203.ipynb|03 Etiquetado de Secuencias Parte 3]]



{{:materias:pln:2019:errorsmeme.png?direct&400|}}

  * Clasificación de texto
    * [[https://www.youtube.com/watch?v=kxImnFg4ZiQ&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm&index=24|Qué es clasificación de texto]]
    * [[https://www.youtube.com/watch?v=j39c7Gjx2gE&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm&index=25|Naive Bayes]]
    * [[https://www.youtube.com/watch?v=VNEdufXVMaU&index=26&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm|Formalizando el clasificador Naive Bayes]]
    * [[https://www.youtube.com/watch?v=3jR8TZG8T88&index=27&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm|Aprendizaje de Naive Bayes]]
    * [[https://www.youtube.com/watch?v=LRFdF9J__Tc&index=28&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm|Relación de Naive Bayes con modelado de lenguaje]]
    * [[https://www.youtube.com/watch?v=OWGVQfuvNMk&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm&index=29|Multinomial Naive Bayes: Ejemplo completo]]
    * [[https://www.youtube.com/watch?v=81j2nzzBHUw&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm&index=30|Precision, recall y F1]]
    * [[https://www.youtube.com/watch?v=TdkWIxGoiak&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm&index=31|Evaluación de clasificación de texto]]


  * Análisis de Sentimiento:
    * [[https://www.youtube.com/watch?v=vy0HC5H-484&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm&index=33|What is Sentiment Analysis]]
    * [[https://www.youtube.com/watch?v=Dgqt62RQMaY&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm&index=34|Sentiment Analysis-  A baseline algorithm]]
    * [[https://www.youtube.com/watch?v=wBE0FE_2ddE&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm&index=35|Sentiment Lexicons]]
    * [[https://www.youtube.com/watch?v=Z7RxBcpyN1U&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm&index=36|Learning Sentiment Lexicons]]



===== 3ra clase =====


  * Estrategias para Machine Learning:
    * [[https://docs.google.com/presentation/d/e/2PACX-1vSjH0TlJzJpY3JeWWY_vPQpHUQnOzcg9cEMLzAcXj8cnm00l8G9_2a9L8eyB6aDWlpUgS0dOTE88j4y/pub?start=false&loop=false&delayms=3000|Parte 1]]
    * [[https://docs.google.com/presentation/d/e/2PACX-1vQkXr831rL-O4iqzsWN1a7vqGoXSww-5qfdHomFu5AF5_hWC9QBo984Il92jbQ3LLvQmF73Pksib13m/pub?start=false&loop=false&delayms=3000|Parte 2]]

  * Notebooks:
    * [[https://github.com/PLN-FaMAF/pln-uba-2019/blob/master/notebooks/sentiment/01%20Baseline.ipynb|01 Baseline]]
    * [[https://github.com/PLN-FaMAF/pln-uba-2019/blob/master/notebooks/sentiment/02%20Bag%20of%20Words.ipynb|02 Bag of Words]]
    * [[https://github.com/PLN-FaMAF/pln-uba-2019/blob/master/notebooks/sentiment/03%20Clasificador%20Basico.ipynb|03 Clasificador Basico]]
    * [[https://github.com/PLN-FaMAF/pln-uba-2019/blob/master/notebooks/sentiment/04%20Modelos%20de%20Clasificacion.ipynb|04 Modelos de Clasificacion]]
    * [[https://github.com/PLN-FaMAF/pln-uba-2019/tree/master/notebooks/sentiment|Más...]]

  * Charla de Rodrigo Loredo en el marco del seminario LIIA.

  * Material complementario:
    * [[https://www.deeplearning.ai/machine-learning-yearning/|Andrew Ng. “Machine Learning Yearning”. Draft, 2018.]]
    * [[https://karpathy.github.io/2019/04/25/recipe/|A Recipe for Training Neural Networks (Andrej Karpathy)]]
    * [[https://github.com/EpistasisLab/tpot|TPOT: A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.]]


===== 4ta clase =====

/*
    * [[http://web.stanford.edu/class/cs224n/lectures/lecture1.pdf|Introduction to NLP and Deep Learning]] ([[https://cs.famaf.unc.edu.ar/~francolq/uba2018/lecture1-2.pdf|versión corta]], [[https://www.youtube.com/watch?v=OQQ-W_63UgQ&t=2725s&index=1&list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6|videolecture]])
*/

  * Word Vectors:
    * [[http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture01-wordvecs1.pdf|Word Vectors 1]] ([[https://youtu.be/8rXD5-xhemo|videolecture]])
    * [[http://web.stanford.edu/class/cs224n/slides/cs224n-2019-lecture02-wordvecs2.pdf|Word Vectors 2]] ([[https://youtu.be/kEMJRjEdNzM|videolecture]])

  * Material complementario:
    * [[https://docs.google.com/document/d/18NoNdArdzDLJFQGBMVMsQ-iLOowP1XXDaSVRmYN0IyM/edit|Frontiers in Natural Language Processing Expert Responses:]] encuesta a referentes del área. En particular:
      * What would you say is the most influential work in NLP in the last decade, if you had to pick just one?



===== 5ta clase =====

  * Introducción a Redes Neuronales:
    * [[https://www.youtube.com/watch?v=8CWyBNX6eDo&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=3|Neural Networks (cs224n lecture 3)]]
    * [[https://www.youtube.com/watch?v=yLYHDSv-288&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=4|Backpropagation (cs224n lecture 4)]]

  * Análisis sintáctico:
    * [[https://youtu.be/nC9_RfjYwqA|Linguistic Structure: Dependency Parsing (cs224n lecture 5)]]

  * Redes Neuronales Recurrentes:
    * [[https://www.youtube.com/watch?v=iWea12EAu6U&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=6|Language Models and RNNs (cs224n lecture 6)]]
    * [[https://www.youtube.com/watch?v=QEw0qEa0E50&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=7|Vanishing Gradients, Fancy RNNs (cs224n lecture 7)]]

  * Links:
    * [[http://colah.github.io/posts/2015-08-Understanding-LSTMs/|Understanding LSTM Networks]]


===== 6ta clase =====

{{:materias:pln:2019:deepmeme.png?direct&400|}}

  * Traducción Automática y modelos "sequence to sequence":
    * [[https://www.youtube.com/watch?v=XXtpJxZBa2c&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=8| Translation, Seq2Seq, Attention (cs224n lecture 8)]]


  * [[https://www.youtube.com/watch?v=yIdF-17HwSk&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=11|Question Answering (cs224n lecture 10)]]


  * [[https://www.youtube.com/watch?v=S-CspeZ8FHc&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=14|Contextual Word Embeddings (cs224n lecture 13)]]


  * Material complementario:
    * [[http://ruder.io/deep-learning-nlp-best-practices/index.html|Deep Learning for NLP Best Practices
]] (Sebastian Ruder)
    * [[http://nlp.seas.harvard.edu/2018/04/03/attention.html|The Annotated Transformer]] (Alexander Rush)
    * [[https://talktotransformer.com/]]
    * [[http://ruder.io/4-biggest-open-problems-in-nlp/|The 4 Biggest Open Problems in NLP
]] (Sebastian Ruder)