Lectures related to Quantum Chemistry Applications

Principal Component Analysis: Dimensionality Reduction

Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.

Machine Learning: Basics and Applications

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

Machine Learning Fundamentals

Covers key concepts and examples of machine learning algorithms and techniques.

Machine Learning Fundamentals

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

Advanced Machine Learning: Fundamentals and Applications

Covers the fundamentals of advanced machine learning, emphasizing practical applications through interactive exercises and projects.

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Supervised Learning Overview

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

Unsupervised Learning: Clustering

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

Clustering: Theory and Practice

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

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.

Clustering: Unsupervised Learning

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

Supervised Learning: Classification and Regression

Covers supervised learning, classification, regression, decision boundaries, overfitting, Perceptron, SVM, and logistic regression.

Introduction to Machine Learning: Linear Models

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Supervised Learning with kNN: Regression Model

Covers a simple mathematical model for supervised learning with k-nearest neighbors in regression.

Machine Learning Basics: Supervised Learning

Introduces the basics of supervised machine learning, covering types, techniques, bias-variance tradeoff, and model evaluation.

Supervised Learning: Linear Regression

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

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.

Machine Learning Fundamentals Log in to Mediaspace to watch this video

Introduces machine learning basics, performance metrics, optimization techniques, and model evaluation.

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.

Machine Learning Fundamentals: Structure Discovery, Classification, Regression Log in to Mediaspace to watch this video

Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.