When to Use Machine Learning

Related lectures (32)

Page 2 of 4

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

Explores the evolution from data analysis to AI and ML, emphasizing big data, machine learning, and social media interaction.

Provides an overview of deep learning concepts, focusing on data, model architecture, and challenges in handling large datasets.

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.

Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.

Explores the MODNet methodology for material property predictions, emphasizing feature selection and supervised learning.

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

Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.