Understanding Distance Metrics in Data Mining - Spring 2023
Explore various distance metrics in data mining, from Euclidean and Manhattan to Mahalanobis and Angular, learning their applications, limitations, and relationships to LSH algorithms.
Bayes Rule and Its Applications in Machine Learning - Lecture 5
Master Bayes' Rule fundamentals through practical examples and discover its crucial applications in machine learning for calculating model probabilities from data.
Data Mining: Embeddings and Representations in Machine Learning - Spring 2023
Explore data representation techniques from TF-IDF to contextualized embeddings, covering word analogies, societal biases, and image representations for advanced data mining applications.
Online Learning and Mistake Bound Analysis in Machine Learning - Lecture 7
Delve into online learning algorithms and performance quantification through mistake bound analysis, focusing on practical implementation strategies and evaluation methods.
Online Learning and Mistake Bound Analysis in Machine Learning - Lecture 8
Delve into online learning algorithms and performance quantification through mistake bound analysis, focusing on practical implementation strategies and evaluation methods.
Data Mining: Understanding Hierarchical Clustering - Spring 2023
Master hierarchical clustering techniques, from basic concepts to advanced validation methods, including cluster proximity measures, stopping criteria, and efficiency considerations.
Perceptron Algorithm and Mistake Bound Theorem - Lecture 9
Dive into advanced Perceptron concepts and mistake bound theorem, building fundamental understanding of this essential machine learning algorithm.
Exponential Family and Information Theory in Probability Distributions - Lecture 8
Dive into advanced statistical concepts including exponential families, multivariable Gaussian distributions, and information theory fundamentals for data science applications.
K-means Clustering and Validation Methods in Data Mining - Spring 2023
Master clustering algorithms through k-means, k-medoids, and Lloyd's algorithm, including initialization techniques, validation methods, and performance metrics for effective data mining applications.
Perceptron Algorithm and the Novikoff-Block Mistake-Bound Theorem - Lecture 10
Delve into the Perceptron algorithm and explore the Novikoff-Block mistake-bound theorem through detailed mathematical proofs and comprehensive analysis.
Least Mean Square Regression for Machine Learning - Lecture 11
Dive into least mean square regression, exploring loss function minimization and gradient descent methods for effective linear regression implementation and optimization.
Generalized Linear Models and Linear Regression - Lecture 11
Dive into advanced statistical concepts including generalized linear models, nonlinear basis functions, and regularization techniques for robust data analysis and point estimation.
Predictive Distribution and Model Comparison in Linear Models - Lecture 12
Dive into predictive distribution modeling, exploring linear models, binary classification, and logistic regression while mastering maximum molecular estimation techniques.
Data Mining: Sketch Algorithms and Count-Min Sketch Analysis - Spring 2023
Explore advanced sketch algorithms including Misra-Gries and Count-min techniques for efficient data mining, with detailed analysis and practical applications.
Nonlinear Regression and Regularization in Data Mining - Spring 2023
Master nonlinear regression concepts, from polynomial mappings to regularization techniques, while learning practical implementation in Python and understanding cross-validation for model optimization.
Probabilistic Machine Learning - Lecture 19
Dive into advanced data science concepts through comprehensive lecture coverage of key theoretical and practical aspects in this graduate-level academic session.
Data Mining: Frequent Directions and Random Projections - Spring 2023
Explore dimensionality reduction techniques through random projections, frequent directions, and SVD, with practical applications including the Linformer architecture for data mining.
Probabilistic Graphical Models and Mean Field Inference - Lecture 20
Dive into advanced data science concepts including local version inference, alternating updates, and mean field version updates for practical applications in graphic models and word distribution.
Multidimensional Scaling and Linear Discriminant Analysis in Data Mining - Spring 2023
Explore multidimensional scaling (MDS) and linear discriminant analysis (LDA), learning key concepts, motivations, and practical applications in data dimensionality reduction and classification.
Distance Metric Learning and Outlier Detection in Data Mining - Spring 2023
Delve into advanced data mining concepts, focusing on distance metric learning, outlier detection techniques, and practical applications of DBSCAN for effective data analysis.
Matrix Completion and PageRank in Data Mining - Spring 2023
Dive into matrix completion techniques and PageRank algorithms, exploring fundamental concepts for data mining and graph analysis applications.