Lectures related to Contrastive losses: Word2Vec and Skip-gram

Covers deep generative models, including variational autoencoders, GANs, and deep convolutional GANs.

Covers the basics of machine learning, challenges in deployment, adversarial attacks, and privacy concerns.

Delves into neural network approximation, supervised learning, challenges in high-dimensional learning, and deep learning experimental revolution.

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

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

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.