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Lecture
Variational Inference and Neural Networks
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Related lectures (55)
Logistic Regression: Probabilistic Interpretation
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Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.
Basics of Linear Regression
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Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.
Supervised Learning Essentials
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Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
Regression Trees and Ensemble Methods in Machine Learning
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Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.
Machine Learning Review
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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.
Graphs in Deep Learning: Applications and Techniques
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Explores the role of graphs in deep learning, focusing on their structure, applications, and techniques for processing graph data.
Statistics: Exploratory Data Analysis
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Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.
Generalized Linear Models: A Brief Review
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Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.
Deep Learning: Designing Neural Network Models
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Covers the design and optimization of neural network models in deep learning.
Linear Classification: Logistic Regression
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Covers linear classification using logistic regression, regularization, and multiclass classification.
Logistic Regression: Interpretation & Feature Engineering
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Covers logistic regression, probabilistic interpretation, and feature engineering techniques.
Linear Models: Continued
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Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.
Decision Trees: Classification
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Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.
Understanding Surface Integrals
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Explores surface integrals, emphasizing physical interpretation and mathematical calculations in vector fields and domains.
Projections and Symmetries
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Explores projections on lines and symmetries in 2D space, emphasizing fixed points and symmetric matrices.
Applied Biostatistics: Bivariate Data and Regression Analysis
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Covers bivariate data analysis, correlation, and regression techniques, including interpretation of coefficients and least squares geometry.
Probability Theory: Basics and Applications
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Covers the fundamental concepts of probability theory and random variables.
Data Representation: BoW and Imbalanced Data
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Covers overfitting, model selection, validation, cross-validation, regularization, kernel regression, and data representation challenges.
Dimensionality Reduction: PCA & t-SNE
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Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.
Variance of Random Variables
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Covers the concept of variance for random variables and introduces calculation rules.
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