Machine Learning: Fundamentals and Applications

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Related lectures (51)

Unsupervised Learning: Dimensionality Reduction and Clustering Log in to Mediaspace to watch this video

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

Introduction to Machine Learning: Course Overview and Basics Log in to Mediaspace to watch this video

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Machine Learning: Supervised and Unsupervised Learning Techniques Log in to Mediaspace to watch this video

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Introduction to Image Classification Log in to Mediaspace to watch this video

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.

Clustering & Density Estimation Log in to Mediaspace to watch this video

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

Data Mining: Introduction Log in to Mediaspace to watch this video

Covers the challenges and opportunities of data mining, practical questions, algorithm components, and applications like shopping basket analysis.

Machine Learning Fundamentals: Structure Discovery, Classification, Regression Log in to Mediaspace to watch this video

Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.

Clustering & Density Estimation Log in to Mediaspace to watch this video

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

Image Classification: Decision Trees & Random Forests Log in to Mediaspace to watch this video

Explores image classification using decision trees and random forests to reduce variance and improve model robustness.

Predicting Rainfall: Miniproject BIO-322 Log in to Mediaspace to watch this video

Introduces a miniproject where students predict rainfall in Pully using machine learning, focusing on reproducibility and code quality.

Untitled Log in to Mediaspace to watch this video

Clustering & Density Estimation Log in to Mediaspace to watch this video

Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.

Regression Trees and Ensemble Methods in Machine Learning Log in to Mediaspace to watch this video

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Clustering: Unsupervised Learning Log in to Mediaspace to watch this video

Covers clustering algorithms, evaluation methods, and practical applications in machine learning.

Unsupervised Learning: Clustering and Dimension Reduction Log in to Mediaspace to watch this video

Covers unsupervised learning, clustering, and dimension reduction techniques.

Data-Driven Modeling in Neuroscience: Meenakshi Khosla Log in to Mediaspace to watch this video

By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.

Deep Learning: Multilayer Perceptron and Training Log in to Mediaspace to watch this video

Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.

Topic Models: Latent Dirichlet Allocation Log in to Mediaspace to watch this video

Covers topic models, focusing on Latent Dirichlet Allocation, clustering, GMMs, Dirichlet distribution, LDA learning, and applications in digital humanities.

Neural Networks Recap: Activation Functions Log in to Mediaspace to watch this video

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Model Evaluation Log in to Mediaspace to watch this video

Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.

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