Machine Learning Fundamentals

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (31)

Page 3 of 4

Covers the fundamental concepts of machine learning, including classification, algorithms, optimization, supervised learning, reinforcement learning, and various tasks like image recognition and text generation.

Neural Networks: Multilayer Learning

Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.

Introduction to Machine Learning

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

Hand Pose Estimation

Covers hand pose estimation, regression techniques, and the evolution of image classification models from LeNet to VGG19.

Deep Learning: Edge Detection and Neural Networks

Discusses edge detection techniques and the evolution of deep learning in neural networks.

Deep Learning: Graphs and Transformers Overview

Covers deep learning concepts, focusing on graphs, transformers, and their applications in multimodal data processing.

Financial Time Series Analysis

Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.

Neural Networks Optimization

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

Deep Learning Fundamentals

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

Uncertainty Quantification and Label Error Detection

Explores uncertainty quantification and label error detection in deep learning for semantic segmentation, focusing on challenges and methods for error detection.