Introduction to Machine Learning

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Deep Splines: Unifying Framework for Deep Neural Networks

Introduces a functional framework for deep neural networks with adaptive piecewise-linear splines, focusing on biomedical image reconstruction and the challenges of deep splines.

Gradient-Based Algorithms in High-Dimensional Learning

Provides insights on gradient-based algorithms, deep learning mysteries, and the challenges of non-convex problems.

Brain Intelligence: Continual Learning of Representational Models

Delves into the continual learning of representational models in brain intelligence, emphasizing rapid adaptation to unstructured environments.

Deep Learning: Dimensionality and Data Representation

Delves into deep learning's dimensionality, data representation, and performance in classifying large-dimensional data, exploring the curse of dimensionality and the neural tangent kernel.

Gradient Descent on Two-Layer ReLU Neural Networks

Analyzes gradient descent on two-layer ReLU neural networks, exploring global convergence, regularization, implicit bias, and statistical efficiency.

Improving Models of the Ventral Visual Pathway

Explores computational models of the ventral visual system, focusing on optimizing networks for real-world tasks and comparing to brain data.

Introduction to Machine Learning: Course Overview and Basics

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

Machine Learning and Modern AI: SWOT Analysis

Covers a SWOT analysis of Machine Learning and Artificial Intelligence, exploring strengths, weaknesses, opportunities, and threats in the field.

Introduction to Machine Learning

Introduces machine learning concepts, from basics to advanced neural networks.

Machine Learning Fundamentals

Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.