Lectures related to Self-supervised learning

Generative Models: Self-Attention and Transformers

Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.

Machine Learning Biases

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

Unsupervised Learning: Clustering

Explores unsupervised learning through clustering techniques, algorithms, applications, and challenges in various fields.

Self-Supervised Image Representation Learning

Presents BYOL, a self-supervised image representation learning method achieving state-of-the-art results without negative pairs.

Self-supervised Learning for Autonomous Vehicles

Explores self-supervised learning for autonomous vehicles, deriving labels from data itself and discussing its applications and challenges.

Introduction to Machine Learning

Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.

Image Representation Insights

Explores the evolution of image representation, challenges in supervised learning, benefits of self-supervised learning, and recent advancements in SSL.

Gradient Descent and Linear Regression

Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.

Supervised Learning Overview

Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.

Introduction to Supervised Learning

Introduces supervised learning using labeled data points to optimize classifier output.

Contrastive losses: Word2Vec and Skip-gram

Covers contrastive losses in Word2Vec and Skip-gram models, negative sampling, Noise Contrastive Estimation, and InfoNCE/CPC.

Non-Linear Dimensionality Reduction

Covers non-linear dimensionality reduction techniques using autoencoders, deep autoencoders, and convolutional autoencoders for various applications.

Dimensionality Reduction: PCA & Autoencoders

Explores PCA, Autoencoders, and their applications in dimensionality reduction and data generation.

Introduction to Machine Learning

Introduces key machine learning concepts, such as supervised learning, regression vs. classification, and the K-Nearest Neighbors algorithm.

Different types of learning

Covers supervised, unsupervised, and reinforcement learning in neurorobotics.

Regression Trees and Ensemble Methods in Machine Learning Log in to Mediaspace to watch this video

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

Supervised Learning: Classification Algorithms Log in to Mediaspace to watch this video

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Supervised Learning: Regression Methods Log in to Mediaspace to watch this video

Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

Introduction to Image Classification Log in to Mediaspace to watch this video

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

Supervised Learning in Asset Pricing Log in to Mediaspace to watch this video

Explores supervised learning in asset pricing, focusing on stock return prediction challenges and model assessment.