Data Mining: Introduction

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Related lectures (32)

Page 2 of 4

Machine Learning: Fundamentals and Applications

Introduces machine learning basics, covering data segmentation, clustering, classification, and practical applications like image classification and face similarity.

Clustering & Density Estimation

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

Machine Learning Fundamentals

Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.

Clustering: K-means & LDA

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

Clustering Methods

Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.

Supervised Learning Overview

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

Clustering: k-means

Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.

Introduction to Data Science

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Feature Extraction & Clustering Methods

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.

Machine learning: Physics and Data

Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.