Lecture
Statistical Inference and Machine Learning
Related lectures (74)
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Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.
Data Representation: PCA
Covers data representation using PCA for dimensionality reduction, focusing on signal preservation and noise removal.
Kernel Regression: Weighted Average and Feature Maps
Covers kernel regression and feature maps for data separability.
Feature Engineering: Polynomial Regression
Covers fitting linear regression on features of the original predictors for flexible feature representation.
Supervised Learning: Classification Algorithms
Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.
Generalization Theory
Explores generalization theory in machine learning, addressing challenges in higher-dimensional spaces and the bias-variance tradeoff.
Kernel Methods: Neural Networks
Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.
Logistic Regression: Fundamentals and Applications
Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.
Supervised Learning: Linear Regression
Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.
Multiclass Classification
Covers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.