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Lecture
Linear Models: Continued
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Related lectures (53)
Convex Optimization: Examples of Convex Functions
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Explores convex optimization, convex functions, and their properties, including strict convexity and strong convexity, as well as different types of convex functions like linear affine functions and norms.
Gradient Descent: Optimization Techniques
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Explores gradient descent, loss functions, and optimization techniques in neural network training.
Linear Models: Basics
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Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.
Linear and Logistic Regression
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Introduces linear and logistic regression, covering parametric models, multi-output prediction, non-linearity, gradient descent, and classification applications.
Linear Models & k-NN
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Covers linear models, logistic regression, decision boundaries, k-NN, and practical applications in authorship attribution and image data analysis.
Linear Models: Part 2
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Covers linear models, binary and multi-class classification, and logistic regression with practical examples.
Machine Learning Fundamentals: Regularization and Cross-validation
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Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.
Logistic Regression: Classification
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Covers supervised learning, classification using logistic regression, and challenges in optimization.
Untitled
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Data-Driven Modeling: Regression
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Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.
Deep Learning: Data Representations and Neural Networks
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Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.
Supervised Learning Fundamentals
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Introduces the fundamentals of supervised learning, including loss functions and probability distributions.
Modern Regression: Statistical Models and Data Analysis
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Introduces regression analysis, covering linear and nonlinear models, Poisson regression, and failure time analysis using various datasets.
Decision Trees and Random Forests: Concepts and Applications
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Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.
Modern Regression: Random Effects and Model Checking
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Explores random effects, model checking, and nested vs. crossed effects in modern regression modeling.
Document Analysis: Topic Modeling
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Explores document analysis, topic modeling, and generative models for data generation in machine learning.
Feature Engineering: Polynomial Regression
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Covers fitting linear regression on features of the original predictors for flexible feature representation.
Kernel Regression: K-nearest Neighbors
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Covers the concept of kernel regression and K-nearest neighbors for making data linearly separable.
Neural Networks: Basics and Applications
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Explores neural networks basics, XOR problem, classification, and practical applications like weather data prediction.
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.
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