Lecture
Machine Learning Review
Related lectures (193)
Neural Networks Recap: Activation Functions
Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.
Kernel Methods: Neural Networks
Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.
Feature Expansion and Kernels
Covers feature expansion, kernels, SVM, and nonlinear classification in machine learning.
Unsupervised Learning: Clustering & Dimensionality Reduction
Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.
Nonlinear ML Algorithms
Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
Neural Networks: Multilayer Perceptrons
Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
Dimensionality Reduction: PCA & t-SNE
Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.
Logistic Regression: Fundamentals and Applications
Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.
Logistic Regression: Vegetation Prediction
Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.
Neural Networks: Training and Optimization
Explores neural network training, optimization, and environmental considerations, with insights into PCA and K-means clustering.