Machine learning: Physics and Data

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Related lectures (31)

Page 3 of 4

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

Document Analysis: Topic Modeling

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Linear Models: Part 1

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

Linear Regression: Introduction and Training

Covers linear regression training to find the best line for given data points, essential for predicting house prices.

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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.

Overfitting: Symptoms and Characteristics

Explores overfitting in polynomial regression, emphasizing the importance of generalization in machine learning and statistics.

Linear Regression: Basics and Applications

Covers the basics of linear regression in machine learning, exploring its applications in predicting outcomes like birth weight and analyzing relationships between variables.

Nonlinear ML Algorithms

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