Introduction to Financial Markets and Time Series

Related lectures (32)

Page 3 of 4

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

Covers the construction of confidence intervals for a normal distribution with unknown mean and variance.

Explores the distribution theory of least squares estimators in a Gaussian linear model, focusing on precision and confidence intervals construction.

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

Covers neural word embeddings and methods for learning word representations in natural language processing.

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

Covers error estimation in Latin Hypercube Sampling, emphasizing the importance of accurate variance estimation.

Provides an overview of hypothesis testing and confidence intervals in statistics, including practical examples and key concepts.

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Covers the training of a binary sentiment classifier using an RNN.