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ДЗКвизы
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06.02.2024Вводное занятие
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Unit 1: Introduction and Basic Text Processing
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13.02.2024Lecture 01:Theories:Тест 1. Introduction to NLP
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NLP Research Questions and Tasks
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Math & Lingustics Background
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Chomsky Hierarchy of Grammars and Automata
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Text Segmentation
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Tokenization and Stemming
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Morphology and Universal Morphology Corpus
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Word frequncies and Zipf’s Law
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Collocations and Multi-word Expressions
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16.02.2024Practice:
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Python Programming & NumPy & Jupyter Notebook
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NLTK
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Unit 2: NLP Techniques
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20.02.2024Lecture 02:
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Theories:Тест 2. Machine Learning basics and Text Classification
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Machine Learning basics
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Classifiers, Logistic Regressions
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Stochastic Gradient Descend
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Vector Space Models and TF-IDFs
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Text Classification
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Sentiment Analysis
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23.02.2024Practice:
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PyTorch & Logistic Regression
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Assignment 1: Word2Vec
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27.02.2024Lecture 03:ДЗ 1 word2vecТест 3. Word Embedding
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Theories:
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Distributional Semantics and Word Embeddings
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Word2Vec and Evaluation
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Softmax and Cross-entropy Loss
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GLoVe, Fasttext
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07.03.2024Practice:
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18:40Word2Vec, Doc2Vec
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Assignment:
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1st assignment is open.
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05.03.2024Lecture 04:Тест 4. Convolutional Neural Networks
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Theories:
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Artificial Neural Networks (ANNs)
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Multilayer Perceptrons (MLPs)
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Backpropagation
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Convolutional Neural Networks (CNN)
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15.03.2024Practice:
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Text Classification with CNNs
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12.03.2024Lecture 05:ДЗ 2 классификация тестовТест 5. Hidden Markov Models and Tagging
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Theories:
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Part-of-Speech (POS) Tagging
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Named Entity Recognition (NER)
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Maximum Entropy (ME)
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Sequence Labelling
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Hidden Markov Models (HMMs)
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Viterbi Search and Forward-Backword Algorithm
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Conditional Random Fields (CRFs)
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22.03.2024Practice:
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Topiс Modeling and Visualization
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19.03.2024Lecture 06:Тест 6. Recurrent Neural Networks
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Theories:
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Neural Language Models
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Recurrent Neural Networks (RNNs)
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Long Short Term Memory (LSTM) Units
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Bi-LSTM-CRF Models for Sequence Labeling
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29.03.2024Practice:
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Neural Networks Tips and Tricks
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Regularizations
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Dropout
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Initialization
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Assignments:
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Assignment 1 Answers
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26.03.2024Lecture 07:
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Theories:Тест 7. Topic Modeling
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Topic Modeling
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Treebanks
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Probabilistic Phrase Structure Grammars (PCFGs)
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Constituent Parings with PCFG
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Dependency Parsing
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Parsing with Neural Networks
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Semantic Role Labeling (optional)
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Coreference Resolution (optional)
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Discourse Parsing (optional)
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05.04.2024Practice:
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Final Project Selection
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Unit 3: NLP Applications
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02.04.2024Lecture 08:
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Theories:Тест 8. Statistical Machine Translation
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Statistical Machine Translation
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Statistical Language Models
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IBM Models
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Log-linear Framework and Phrase-based Models
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Beam Search Decoding
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Machine Translation Evaluation and BLEU
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Sequence-to-sequence Models
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Attention Mechanisms
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RNN-based Neural Machine Translation (NMT)
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12.04.2024Practice:
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RNN-based NMT
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09.04.2024Lecture 09:Тест 9. Transformers
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Theories:
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Subword Level and Character Level NMT
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Transformers