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Words, tokens, n-grams and Language Models
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Related lectures (38)
Common Distributions: Moment Generating Functions
Explores common probability distributions, special distributions, and entropy concepts.
Probability and Statistics
Covers inequalities, joint Gaussian distribution, risk estimation, and classification method testing in probability and statistics.
Topic Models: Understanding Latent Structures
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Explores topic models, Gaussian mixture models, Latent Dirichlet Allocation, and variational inference in understanding latent structures within data.
Topic Models
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Introduces topic models, covering clustering, GMM, LDA, Dirichlet distribution, and variational inference.
Gaussian Mixture Models: Data Classification
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Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.
Words Tokens: Lexical Level Overview
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Explores words, tokens, and language models in NLP, covering challenges in defining them, lexicon usage, n-grams, and probability estimation.
Lexicons, n-grams and Language Models
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Explores lexicons, n-grams, and language models, emphasizing their importance in recognizing words and the effectiveness of n-grams for various tasks.
Topic Models: Latent Dirichlet Allocation
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Covers topic models, focusing on Latent Dirichlet Allocation, clustering, GMMs, Dirichlet distribution, LDA learning, and applications in digital humanities.
Data Analysis: Correlation Measures
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Covers the basics of data analysis, focusing on statistical concepts and correlation measures.
Air Pollution Analysis
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Explores air pollution analysis using wind data, probability distributions, and trajectory models for air quality assessment.
Topic Models: Latent Dirichlet Allocation
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Introduces Latent Dirichlet Allocation for topic modeling in documents, discussing its process, applications, and limitations.
Estimating R: Moments of a Distribution
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Explains the importance of moments in measuring distribution properties, such as expectation and variance.
Introduction to Physics: Understanding Natural Phenomena
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Covers the basics of physics, emphasizing understanding natural phenomena and the role of mathematics in representing physical laws.
Advanced Physics I: Brayton Cycle and Air Density
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Explores the Brayton cycle, air density variations, and forces in motion.
Machine Learning Fundamentals
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Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Gaussian Mixture Models: Likelihood and Covariance Matrix
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Explores statistical independence, Gaussian Mixture Models, and fitting data with Gaussian functions.
Orthogonality and Least Squares
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Introduces orthogonality between vectors, angles, and orthogonal complement properties in vector spaces.
Linear Regression: Estimation and Prediction
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Covers the basics of linear regression, focusing on estimation and prediction.
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