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Machine Learning Basics

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Related lectures (50)

Logistic Regression: Interpretation & Feature Engineering Log in to Mediaspace to watch this video

Covers logistic regression, probabilistic interpretation, and feature engineering techniques.

Deep Learning: Designing Neural Network Models Log in to Mediaspace to watch this video

Covers the design and optimization of neural network models in deep learning.

Machine Learning Fundamentals: Overfitting and Regularization Log in to Mediaspace to watch this video

Covers overfitting, regularization, and cross-validation in machine learning, exploring polynomial curve fitting, feature expansion, kernel functions, and model selection.

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.

Supervised Learning: Regression Methods Log in to Mediaspace to watch this video

Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

Machine Learning for Physicists/Chemists: Image Classification Log in to Mediaspace to watch this video

Covers the fundamentals of machine learning for physicists and chemists, focusing on image classification tasks using artificial intelligence.

Model Evaluation Log in to Mediaspace to watch this video

Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.

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.

Overfitting in Supervised Learning: Case Studies and Techniques Log in to Mediaspace to watch this video

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Deep Learning: Multilayer Perceptron and Training Log in to Mediaspace to watch this video

Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.

Image Classification: Decision Trees & Random Forests Log in to Mediaspace to watch this video

Explores image classification using decision trees and random forests to reduce variance and improve model robustness.

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.

Polynomial Regression and Gradient Descent Log in to Mediaspace to watch this video

Covers polynomial regression, gradient descent, overfitting, underfitting, regularization, and feature scaling in optimization algorithms.

Model Selection and Evaluation Log in to Mediaspace to watch this video

Discusses the experimental framework for selecting and evaluating supervised learning models to prevent overfitting.

Kernel Methods in Machine Learning: Kernel Regression and SVM Log in to Mediaspace to watch this video

Discusses kernel methods in machine learning, focusing on kernel regression and support vector machines, including their formulations and applications.

Model Assessment: Metrics and Selection Log in to Mediaspace to watch this video

Explores model assessment metrics, selection techniques, bias-variance tradeoff, and handling skewed data distributions in machine learning.

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.

Receiver-Operator Characteristics: ROC Curves Log in to Mediaspace to watch this video

Explains ROC curves, Precision-Recall curve, RMSLE, and model validation.

Model Selection: Evaluation and Generalization Log in to Mediaspace to watch this video

Explores model selection, evaluation, and generalization in machine learning, emphasizing unbiased performance estimation and the risks of over-learning.

Model Selection and Evaluation: Bias-Variance Dilemma, Ridge Estimation Log in to Mediaspace to watch this video

Explores over-learning, generalization, and under-learning in machine learning models.

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