Advanced Spark Optimizations and Partitioning

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (31)

Page 1 of 4

Efficient Machine Learning via Data Summarization

Explores efficient machine learning through data summarization, covering challenges, methods, and impactful applications in various domains.

Logistic Regression: Fundamentals and Applications

Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.

Statistical Analysis of Networks: Link Prediction and Biclustering

Explores link prediction, logistic regression, causal inference, and biclustering in statistical network analysis.

Supervised Learning Overview

Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.

Introduction to Machine Learning

Covers the basics of machine learning for physicists and chemists, focusing on image classification and dataset labeling.

Unsupervised Learning: Clustering & Dimensionality Reduction

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Machine Learning Fundamentals: Structure Discovery, Classification, Regression

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

Supervised Learning: Likelihood Maximization

Covers supervised learning through likelihood maximization to find optimal parameters.

Introduction to Image Classification

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

Linear Models for Classification: Multi-Class Extensions

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.