Lecture
Machine Learning Fundamentals
Related lectures (69)
Classification: Introduction
Covers clustering and classification, building models to assign objects to classes based on attribute values.
Optimal Errors and Phase Transitions
Explores optimal errors and phase transitions in high dimensional models.
Introduction to Machine Learning
Introduces fundamental machine learning concepts and algorithms for model training and evaluation.
Unsupervised Learning: Clustering and Dimension Reduction
Covers unsupervised learning, clustering, and dimension reduction techniques.
Convex Optimization: Nonlinear Dimensionality Reduction
Explores convex optimization in nonlinear dimensionality reduction, showcasing practical applications in signal processing and regression tasks.
Deep learning in the IoT contextMOOC: IoT Systems and Industrial Applications with Design Thinking
Delves into deep learning for image and object classification in IoT systems, including clustering techniques and privacy concerns.
Principal Component Analysis: Geometric Interpretation and Dimension Reduction
Explores Principal Component Analysis for dimension reduction and data representation in a new basis.
Introduction to Machine Learning: Basics and Examples
Introduces the basics of machine learning, emphasizing the use of Piazza for class-related communications and practical exercises in Python.
Clustering Methods and Dimensionality Reduction
Covers clustering methods and dimensionality reduction techniques.
Principal Component Analysis: Dimension Reduction
Explores Principal Component Analysis for dimension reduction in datasets and its implications for supervised learning algorithms.