Lecture

Data Representations and Processing in Machine Learning

Related lectures (32)

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Covers data representations, challenges of imbalanced data, and strategies for data normalization and cleaning.

Explores data representations, overfitting, model selection, cross-validation, and imbalanced data challenges.

Explores challenges in data assumptions, biases, and more in research, including incomplete write-ups and frustrations of newcomers.

Discusses overfitting, model selection, cross-validation, regularization, data representations, and handling imbalanced data in machine learning.

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Covers the Boltzmann Machine, a type of stochastic recurrent neural network.

Covers polynomial feature expansion, kernel methods, data representations, normalization, and handling imbalanced data in machine learning.

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Covers data wrangling techniques using Apache Hive for efficient big data management.

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

Covers overfitting, model selection, validation, cross-validation, regularization, kernel regression, and data representation challenges.

Covers sampling, cross-validation, quantifying performance, optimal model determination, overfitting detection, and classification sensitivity.