Lectures related to Regression: Exercises

Supervised Learning: Classification and Regression

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

Kernel Methods and Regression

Covers kernel methods, kernel regression, RBF kernel, and SVM for classification.

Introduction to Machine Learning: Linear Models

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

Neural Network: Random Features and Kernel Regression

Covers random features in neural networks and kernel regression equivalence.

Data Representations: Learning Methods

Covers polynomial feature expansion, kernel functions, regression, and SVM, emphasizing the importance of choosing functions for feature expansion.

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.

Machine Learning Basics: Supervised Learning

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

Introduction to Machine Learning: Supervised Learning

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

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.

Validation and k-Nearest Neighbors Method

Introduces supervised learning concepts and the k-Nearest Neighbors method for classification and regression tasks.

Supervised Learning with kNN: Regression Model

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

Loss Functions and Optimization

Explores loss functions, gradient descent, and step size impact on optimization in machine learning models, highlighting the delicate balance required for efficient convergence.

Optimization Techniques: Gradient Method Overview

Discusses the gradient method for optimization, focusing on its application in machine learning and the conditions for convergence.

Kernel Regression: K-nearest Neighbors Log in to Mediaspace to watch this video

Covers the concept of kernel regression and K-nearest neighbors for making data linearly separable.

Linear and Weighted Regression: Optimal Parameters and Local Solutions Log in to Mediaspace to watch this video

Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.

Nonparametric Regression: Kernel-Based Estimation Log in to Mediaspace to watch this video

Covers nonparametric regression using kernel-based estimation techniques to model complex relationships between variables.

Feature Expansion: Kernels and KNN Log in to Mediaspace to watch this video

Covers feature expansion, kernels, and K-nearest neighbors, including non-linearity, SVM, and Gaussian kernels.

Linear Models: Part 1 Log in to Mediaspace to watch this video

Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.

Machine Learning Review Log in to Mediaspace to watch this video

Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.

Regression: High Dimensions Log in to Mediaspace to watch this video

Explores linear regression in high dimensions and practical house price prediction from a dataset.