Course Catalog
Machine Learning Technologies
95-897
Units: 6
Description
This course offers a foundational introduction to the principles, methodologies, and applications of modern machine learning (ML); from foundational principles to cutting-edge methods across diverse data modalities. It is organized into three broad parts. Part I focuses on the foundations of supervised learning, emphasizing classical models such as linear and logistic regression, decision trees, and ensemble methods. The goal is to equip students with the understanding of the ML workflow, model evaluation, and generalization principles. Core concepts such as bias-variance tradeoff, interpretability, uncertainty, robustness, and fairness are introduced early, preparing students to think critically about model performance and reliability. This part also introduces the contemporary learning paradigms including self-supervised, semi-supervised, active, meta, continual, reinforcement, and federated learning to showcase the diverse settings under which ML is developed.
Building on this foundation, Part II introduces modern models including neural networks and advanced architectures such as convolutional, recurrent, and attention-based models. Finally, Part III focuses on ML techniques for specialized data modalities—tabular, graph, temporal, text, and image data—and their practical applications such as forecasting, recommendation, fraud detection.
By the end of the course, students will have a comprehensive understanding of both the theoretical underpinnings and the practical considerations involved in designing and applying machine learning techniques.
Learning Outcomes
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Explain the fundamental concepts and paradigms of machine learning, including supervised, unsupervised, and semi-supervised learning.
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Apply classical ML algorithms such as linear models, decision trees, and ensemble methods to real-world datasets.
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Evaluate and compare models using appropriate metrics, cross-validation, and techniques for managing overfitting and generalization.
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Understand key concepts associated with ML models, including uncertainty estimation, interpretability, robustness, and fairness.
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Describe and compare neural network architectures, including CNNs, RNNs, and transformers, for diverse tasks.
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Differentiate between learning paradigms such as self-supervised, reinforcement, meta, continual, and federated learning.
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Design ML solutions for specialized data types, including tabular, graph, temporal, text, and image data.
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Critically assess recent developments in foundation and multimodal models and their applications.
Prerequisites Description
This course does not assume any prior exposure to machine learning theory or practice. Students are expected to have the following background: • Basic knowledge of probability • Basic knowledge of linear algebra • Basic programming skills • Familiarity with Python programming and basic use of Scikit-learn, Numpy and Pandas.