Lectures related to Machine Learning Basics

Introduction to Machine Learning

Covers the basics of Machine Learning, including recognizing hand-written digits, supervised classification, decision boundaries, and polynomial curve fitting.

Machine Learning Basics: Supervised Learning

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

Flexibility of Models & Bias-Variance Trade-Off

Delves into the trade-off between model flexibility and bias-variance in error decomposition, polynomial regression, KNN, and the curse of dimensionality.

Nonlinear ML Algorithms

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Introduction to Machine Learning: Supervised Learning

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

Introduction to Machine Learning: Linear Models

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

Generalization Theory

Explores generalization theory in machine learning, addressing challenges in higher-dimensional spaces and the bias-variance tradeoff.

Nearest Neighbor Rules: Part 2

Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.

Overfitting, Cross-validation, Regularization

Explores overfitting, cross-validation, and regularization in machine learning, emphasizing model complexity and the importance of regularization strength.

Bias-Variance Trade-Off Log in to Mediaspace to watch this video

Explores underfitting, overfitting, and the bias-variance trade-off in machine learning models.

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.

Polynomial Regression: Overview Log in to Mediaspace to watch this video

Covers polynomial regression, flexibility impact, and underfitting vs overfitting.

Decision Trees and Random Forests: Concepts and Applications Log in to Mediaspace to watch this video

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.

Model Complexity and Overfitting in Machine Learning Log in to Mediaspace to watch this video

Covers model complexity, overfitting, and strategies to select appropriate machine learning models.

Gaussian Discriminant Rule: Classification & Boundaries Log in to Mediaspace to watch this video

Explores the Gaussian Discriminant Rule for classification using Gaussian Mixture Models and discusses drawing boundaries and model complexity.

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.

Machine Learning Fundamentals Log in to Mediaspace to watch this video

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

Gaussian Naive Bayes & K-NN Log in to Mediaspace to watch this video

Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.

Error Decomposition and Regression Methods Log in to Mediaspace to watch this video

Covers error decomposition, polynomial regression, and K Nearest-Neighbors for flexible modeling and non-linear predictions.

Linear Regression and Logistic Regression Log in to Mediaspace to watch this video

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.