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Επεξεργασία Φυσικής Γλώσσας - Natural Language Processing (MSc CS & MSc ISDS)

INF210 - Ion Androutsopoulos

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Root directory slides_2025_26 The slides of 2025-26.

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	First Name	Size	Date
	nlp_slides_part06_nlp_with_transformers.pdf Transformer encoders, BERT. Encoder-decoder Transformers, BART, T5. Decoder-only Transformers, GPT-x. Prompting, supervised fine-tuning, RLHF, DPO. Parameter efficient training, LoRA. Retrieval augmented generation (RAG), LLMs with tools, agents, ReACT. Adding vision to LLMs, LLaVA, InstructBLIP. Optional slides: Data augmentation for NLP.	6.39 MB	11/22/25, 8:57 PM
	nlp_slides_part05_nlp_with_cnns.pdf Quick background on Convolutional Neural Networks (CNNs) in Computer Vision. Text processing with CNNs. Image to text generation with CNN encoders and RNN decoders.	2.31 MB	11/12/25, 3:16 PM
	nlp_slides_part04_nlp_with_rnns.pdf Recurrent neural networks (RNNs), GRUs/LSTMs. Bidirectional and stacked RNNs. RNNs with self-attention or global max-pooling. RNNs in text and token classification. RNN language models. Obtaining word embeddings from character-based RNNs. Hierarchical RNNs. Sequence-to-sequence RNN models with attention, applications in machine translation. Optional slides: Variational dropout. Universal sentence encoders, LASER. Pre-training RNN language models, ELMo.	3.38 MB	11/12/25, 3:17 PM
	nlp_slides_part03_text_classification_with_mlps.pdf Multi-Layer Perceptrons (MLPs) and backpropagation. Dropout, batch and layer normalization. MLPs for text classification, regression, token classification (e.g., for POS tagging, named entity recognition). Pre-training word embeddings, Word2Vec. Advice for training large neural networks.	2.28 MB	10/23/25, 1:20 PM
	nlp_slides_part02_text_classification_with_mostly_linear_models.pdf Representing documents as bags of words. Boolean and TF-IDF features. Feature selection and extraction using information gain and SVD. Obtaining word embeddings from PMI scores. Word and text clustering with k-means. Text classification with k nearest neighbors. Linear and logistic regression, stochastic gradient descent. Evaluating classifiers with precision, recall, F1, ROC AUC. Practical advice and diagnostics for text classification with supervised machine learning.	3.67 MB	10/23/25, 1:20 PM
	nlp_slides_part01_ngrams.pdf n-gram language models, estimating probabilities from corpora, entropy, cross-entropy, perplexity, edit distance, context-aware spelling correction, beam-search decoding.	2.92 MB	10/14/25, 10:03 AM
	nlp_slides_part00_introduction.pdf Introduction and course organization.	2.12 MB	10/1/25, 1:36 PM