Lectures related to When to Use Machine Learning

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.

Introduction to Machine Learning: Regression Fundamentals

Introduces the fundamentals of regression in machine learning, covering course logistics, key concepts, and the importance of loss functions in model evaluation.

Data Analysis to AI and ML, Social Media

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

Clustering: K-means & LDA

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

Clustering: Theory and Practice

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

Data Driven Science: MODNet Methodology

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

Machine Learning Fundamentals

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

Introduction to Data Stream Processing: Concepts and Applications

Covers the principles of data stream processing and its applications in real-time data analysis.

Big Data Ecosystems: Technologies and Challenges

Covers the fundamentals of big data ecosystems, focusing on technologies, challenges, and practical exercises with Hadoop's HDFS.

General Introduction to Data Science

Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.

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 Preparation for Machine Learning: Categorical and Numerical Data Log in to Mediaspace to watch this video

Explores data preparation for machine learning, emphasizing categorical to numerical conversion and effective data visualization techniques.

Introduction to Machine Learning Log in to Mediaspace to watch this video

Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

Introduction to Machine Learning: Course Overview and Basics Log in to Mediaspace to watch this video

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Deep Learning: Principles and Applications Log in to Mediaspace to watch this video

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

Data Mining: Introduction Log in to Mediaspace to watch this video

Covers the challenges and opportunities of data mining, practical questions, algorithm components, and applications like shopping basket analysis.

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.

Deep Learning Fundamentals Log in to Mediaspace to watch this video

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

Document Analysis: Topic Modeling Log in to Mediaspace to watch this video

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Unsupervised Learning: Clustering & Dimensionality Reduction Log in to Mediaspace to watch this video

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