Lectures related to Text mining

Textual Data Analysis: Classification & Dimensionality Reduction

Explores textual data classification, focusing on methods like Naive Bayes and dimensionality reduction techniques like Principal Component Analysis.

Digital Humanities Lab: Practices

Introduces students to digital humanities through practical exercises, such as working on a large ongoing project.

Information Extraction: Methods and Applications

Explores methods for information extraction, including traditional and embedding-based approaches, supervised learning, distant supervision, and taxonomy induction.

Sequential pattern mining for robust event detection

Presents a method for event detection from news using limited resources, making it robust to noise and language-independent.

Natural Language Processing Tools

Introduces NLP tools like Hugging Face and PyTorch Lightning for text tasks and evaluation.

Binary Sentiment Classifier Training

Covers the training of a binary sentiment classifier using an RNN.

Quantum Chemistry Fundamentals

Covers the fundamentals of quantum chemistry, including valence bond theory and molecular orbitals.

Quantum Chemistry: Lecture Series

Series covers spherical coordinates, harmonics, Legendre polynomials, and angular momentum.

Spectroscopy Fundamentals

Covers the fundamentals of spectroscopy, including tools, color, Doppler effect, and molecular behavior.

Quantum Chemistry: Wave-Particle Duality

Explores the wave-particle duality in quantum chemistry, covering interference phenomena and energy quantization.

Spectroscopy Fundamentals

Covers the fundamentals of spectroscopy, including selection rules and transitions.

Quantum Chemistry Fundamentals

Covers the basics of quantum chemistry and the Hartree-Fock method.

Model Finetuning for NLI

Covers the second assignment for the CS-552: Modern NLP course, focusing on transfer learning and data augmentation.

Information retrieval: vector space

Covers the basics of information retrieval using vector space models and practical exercises on relevance feedback and posting list scanning.

Entity & Information Extraction

Explores knowledge extraction from text, covering key concepts like keyphrase extraction and named entity recognition.

NLP Pre-processing: Tokenization, Stop Words, Lemmatization

Covers tokenization, stop words removal, and lemmatization for NLP tasks.

Text Data Processing: Basics & Techniques

Introduces the basics of text data processing, covering document retrieval, classification, sentiment analysis, and topic detection.

Matrix Factorization: Linking Text to Knowledge Bases

Explores distant supervision for linking text to knowledge bases using entity extraction and classifiers.

Quantum Chemistry

Covers topics related to quantum chemistry, including observable operators and eigenvalues.

Information Retrieval: Fagin's Algorithm

Covers the implementation of Fagin's algorithm for information retrieval, focusing on efficient document retrieval.