Lectures related to Hypothesis Space and Learning Task

Covers the basics of machine learning, challenges in deployment, adversarial attacks, and privacy concerns.

Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.

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

Gradient Descent and Linear Regression

Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.

Supervised Learning: Linear Regression

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

Biases, ML performance and adversarial ML threats

Explores Machine Learning basics, adversarial conditions, privacy implications, and deployment challenges, highlighting biases and adversarial threats.

Smart, Connected Products

Explores smart, connected products and their transformative impact on companies, covering artificial intelligence, machine learning, predictive models, forecasting methods, and more.

Unsupervised Learning: Clustering

Explores unsupervised learning through clustering techniques, algorithms, applications, and challenges in various fields.

Machine Learning Fundamentals

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

Introduction to Machine Learning

Introduces key machine learning concepts, such as supervised learning, regression vs. classification, and the K-Nearest Neighbors algorithm.

Active Learning for Molecular Design

Covers machine learning approaches for material design, practical examples, and software tools for research.

Machine Learning Basics: Supervised and Unsupervised Learning

Covers the basics of machine learning, supervised and unsupervised learning, various techniques like k-nearest neighbors and decision trees, and the challenges of overfitting.

Machine Learning: Supervised and Unsupervised Learning Techniques Log in to Mediaspace to watch this video

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Supervised Learning: Classification Algorithms Log in to Mediaspace to watch this video

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Binary Classification by Regression: Decision Functions and Cost Functions Log in to Mediaspace to watch this video

Explores binary classification by regression, decision functions, and various cost functions.

Machine Learning at the Atomic Scale Log in to Mediaspace to watch this video

Explores simple models, electronic structure evaluation, and machine learning at the atomic scale.

Regression Trees and Ensemble Methods in Machine Learning Log in to Mediaspace to watch this video

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

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.

Statistical Learning: Fundamentals Log in to Mediaspace to watch this video

Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.

Introduction to Image Classification Log in to Mediaspace to watch this video

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