Lecture
Topic Models
Related lectures (54)
Words, tokens, n-grams and Language Models
Explores words, tokens, n-grams, and language models, focusing on probabilistic approaches for language identification and spelling error correction.
Probability and Statistics
Covers fundamental concepts in probability and statistics, including distributions, properties, and expectations of random variables.
Document Analysis: Topic Modeling
Explores document analysis, topic modeling, and generative models for data generation in machine learning.
Fundamental Limits of Gradient-Based Learning
Delves into the fundamental limits of gradient-based learning on neural networks, covering topics such as binomial theorem, exponential series, and moment-generating functions.
Probability Distributions in Environmental Studies
Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.
Normal Distribution: Properties and Calculations
Covers the normal distribution, including its properties and calculations.
Lexicons, n-grams and Language Models
Explores lexicons, n-grams, and language models, emphasizing their importance in recognizing words and the effectiveness of n-grams for various tasks.
Interval Estimation: Method of Moments
Covers the method of moments for estimating parameters and constructing confidence intervals based on empirical moments matching distribution moments.
Unsupervised Learning: PCA & K-means
Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.
Dimensionality Reduction: PCA and LDA
Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.