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EE-311: Fundamentals of machine learning

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Lectures in this course (48)

Introduction to Machine Learning: Basics and Examples Log in to Mediaspace to watch this video

Introduces the basics of machine learning, emphasizing the use of Piazza for class-related communications and practical exercises in Python.

Introduction to Machine Learning: Basics and Examples Log in to Mediaspace to watch this video

Introduces the basics of machine learning, covering supervised learning, reinforcement learning, and dimension reduction.

Cross-Entropy and Regression Cost Functions Log in to Mediaspace to watch this video

Explores cross-entropy in classification and various regression cost functions.

Generalization and Overfitting Log in to Mediaspace to watch this video

Covers generalization, overfitting, and model complexity in machine learning.

Convex Optimization: Examples of Convex Functions Log in to Mediaspace to watch this video

Explores convex optimization, convex functions, and their properties, including strict convexity and strong convexity, as well as different types of convex functions like linear affine functions and norms.

Convex Optimization: Gradient Algorithms Log in to Mediaspace to watch this video

Covers convex optimization problems and gradient-based algorithms to find the global minimum.

Parametric Regressions: Linear Regression Log in to Mediaspace to watch this video

Explores parametric regressions, emphasizing linear regression's simplicity and complexity trade-offs between parametric and non-parametric models.

Logistic Regression and Polynomial Regression Log in to Mediaspace to watch this video

Introduces logistic regression for binary classification and polynomial regression on polynomial variables.

Support Vector Machines: Definition and Separation Hyperplane Log in to Mediaspace to watch this video

Covers the history, linear separability, hyperplanes, and support vectors in Support Vector Machines.

Support Vector Machine: Primal Formulation with Hard Margin Log in to Mediaspace to watch this video

Covers the Support Vector Machine with a hard margin formulation and the importance of maximizing the margin between classes.

Support Vector Machines: Dual Formulation for Hard Margin Log in to Mediaspace to watch this video

Explores the dual formulation of Support Vector Machines for hard margin classification.

Support Vector Machines: Soft Margin SVM Log in to Mediaspace to watch this video

Introduces Soft Margin SVM, aiming to balance errors and margin width.

Supervised Learning: Image Space and Labeling Log in to Mediaspace to watch this video

Covers supervised learning, binary and multi-class classification problems, and making predictions from labeled examples.

SVM and Multiclass Classification Log in to Mediaspace to watch this video

Covers SVM and multiclass classification using one-vs-all and one-vs-one approaches.

Redescription SVM and Hilbert Spaces Log in to Mediaspace to watch this video

Explores redescription SVM and Hilbert spaces for solving nonlinear problems using inner product spaces and separable Hilbert spaces.

Support Vector Machines: Kernel Tricks Log in to Mediaspace to watch this video

Explores kernel tricks in support vector machines for efficient computation in high-dimensional spaces without explicit transformation.

Dimension Reduction: Curse of Dimensionality Log in to Mediaspace to watch this video

Explores dimension reduction and the curse of dimensionality, highlighting the exponential relation between examples and dimension.

Variable Selection Methods: Filtering and Correlation Log in to Mediaspace to watch this video

Explores variable selection through filtering and correlation methods in machine learning, emphasizing relevance quantification and relationship measurement with the label.

Training and Testing Games Log in to Mediaspace to watch this video

Emphasizes the importance of separating training and testing data for machine learning models.

Variable Selection Methods: Subset vs. Container Approaches Log in to Mediaspace to watch this video

Explores variable selection methods in machine learning, including subset and container approaches, using exhaustive and greedy procedures.

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