The Art of Brainstorming: Techniques and Principles

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Related lectures (32)

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Clustering: Dimensionality Reduction

Explores clustering and dimensionality reduction techniques in finance to clean and simplify data.

Design Thinking: History and Concepts

Delves into the history and concepts of design thinking and its applications.

Clustering: Unsupervised Learning

Explores dimensionality reduction, clustering algorithms, and the state of machine learning.

Clustering: K-Means

Covers clustering and the K-means algorithm for partitioning datasets into clusters based on similarity.

Design Thinking as methodology

Covers Design Thinking methodology, emphasizing empathy, problem definition, ideation, prototyping, and testing for user-centered solutions.

Kernel K-means: Analysis and Applications

Explores Kernel K-means algorithm, its analysis, applications, and limitations in clustering.

Unsupervised Learning: Clustering Methods

Explores unsupervised learning through clustering methods like K-means and DBSCAN, addressing challenges and applications.

Accelerating Innovation through Data

Explores how analogies drive innovation and the challenges of finding analogies in large datasets to automatically discover analogies.

Dimensionality Reduction: PCA and LDA

Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.

Clustering: Hierarchical and K-means Methods

Introduces hierarchical and k-means clustering methods, discussing construction approaches, linkage functions, Ward's method, the Lloyd algorithm, and k-means++.