Lectures in course CS-433: Machine learning

Introduction to Machine Learning: Regression Fundamentals

Introduces the fundamentals of regression in machine learning, covering course logistics, key concepts, and the importance of loss functions in model evaluation.

Loss Functions and Optimization

Explores loss functions, gradient descent, and step size impact on optimization in machine learning models, highlighting the delicate balance required for efficient convergence.

Support Vector Machines: Maximizing Margin

Explores Support Vector Machines, maximizing margin for robust classification and the transition to soft SVM for non-linearly separable data.

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Kernel Ridge Regression and the Kernel Trick

Explores Kernel Ridge Regression, the Kernel Trick, Representer Theorem, feature spaces, kernel matrix, predicting with kernels, and building new kernels.

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Convolutional Neural Networks: Structure & Regularization

Explores Convolutional Neural Networks, data augmentation, weight decay, and dropout for enhancing model performance.

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Adversarial Machine Learning

Explore the vulnerabilities of neural networks to adversarial attacks and the strategies to enhance model robustness.

Bias in Machine Learning

Explores the impact of bias in machine learning models and strategies to mitigate risks.

Ethics and Fairness in Machine Learning

Explores the ethical implications of deploying machine learning algorithms and emphasizes the importance of fairness in decision-making processes.

Optimization: Gradient Descent and Subgradients

Explores optimization methods like gradient descent and subgradients for training machine learning models, including advanced techniques like Adam optimization.

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Latent Factor Analysis: Movie Genre Classification

Explores latent factor analysis for movie genre classification based on male versus female leads.

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