Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Home

Lecture

Advanced Machine Learning: Fundamentals and Applications

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (52)

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.

Data-Driven Modeling: Regression Log in to Mediaspace to watch this video

Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.

Quantum Chemistry Applications Log in to Mediaspace to watch this video

Explores quantum chemistry applications, emphasizing the role of electron density in predicting chemical properties and addressing challenges in catalyst design, solar energy conversion, and drug synthesis.

Classification Algorithms: Generative and Discriminative Approaches Log in to Mediaspace to watch this video

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Feature Extraction & Clustering Methods Log in to Mediaspace to watch this video

Covers feature extraction, clustering, and classification methods for high-dimensional datasets and behavioral analysis using PCA, t-SNE, k-means, GMM, and various classification algorithms.

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.

Edge Detection: Deep Learning Insights Log in to Mediaspace to watch this video

Explores the evolution of edge detection techniques, from Canny to deep learning insights.

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.

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.

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

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

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

Covers unsupervised learning, clustering, and dimension reduction techniques.

Nonlinear Machine Learning: k-Nearest Neighbors and Feature Expansion Log in to Mediaspace to watch this video

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Machine Learning Applications: Regression and Classification Log in to Mediaspace to watch this video

Explores machine learning applications in materials modeling, covering regression, classification, and feature selection.

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 & 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.

Binary Classification by Regression: Decision Functions and Cost Functions Log in to Mediaspace to watch this video

Explores binary classification by regression, decision functions, and various cost functions.

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

Regression: Exercises Log in to Mediaspace to watch this video

Covers exercises on regression functions using RLS, WLS, and LWR.

Feature Selection, Kernel Regression, Neural Networks Playground Log in to Mediaspace to watch this video

Covers feature selection, kernel regression, and neural networks through exercises.

Page 2 of 3