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Related lectures (35)
Linear Regression: Fundamentals and Applications
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Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.
Introduction to Machine Learning: Course Overview and Basics
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Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.
Machine Learning: Supervised and Unsupervised Learning Techniques
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Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.
Linear Regression: Basics
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Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.
Binary Classification by Regression: Decision Functions and Cost Functions
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Explores binary classification by regression, decision functions, and various cost functions.
Linear Regression: Basics and Estimation
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Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.
Linear Regression: Basics and Gradient Descent
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Covers the basics of linear regression, including feature engineering, supervised vs. unsupervised learning, and minimizing the cost function.
Linear Models: Part 1
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Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.
Gradient Descent: Linear Regression
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Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.
Supervised Learning: Likelihood Maximization
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Covers supervised learning through likelihood maximization to find optimal parameters.
Classification Algorithms: Generative and Discriminative Approaches
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Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.
Linear Regression: Beyond the Basics
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Explores advanced concepts in linear regression models, including multicollinearity, hypothesis testing, and handling outliers.
Deep and Convolutional Networks: Generalization and Optimization
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Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.
Unsupervised Learning: Movie Recommendation
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Covers unsupervised learning for movie recommendation using singular value decomposition.
Structure Discovery: Machine Learning for Behavioral Data
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Explores Bayesian Knowledge Tracing, Generalized Linear Models, and clustering algorithms for structure discovery in behavioral data.
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