| (*)Students have a thorough understanding of unsupervised machine learning techniques, equipping them to identify, implement, and adapt methods for clustering, data projection, and reduction in scientific and engineering contexts. They have the ability to interpret the structure within data, understand the mathematical principles underlying unsupervised methods, and apply these techniques to complex real-world problems without predefined targets.
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(*)- Applying Clustering and Data Grouping Methods (k4)
Students can implement and apply basic and advanced clustering techniques, including hierarchical clustering, affinity propagation, and mixture models, to discover patterns and groupings in data.
- Utilizing Projection and Dimensionality Reduction Techniques (k4)
Students are able to use methods like principal component analysis (PCA), independent component analysis (ICA), and factor analysis to reduce data dimensionality while retaining essential structure.
- Implementing Error Models and Expectation Maximization (k3)
Students can work with error models and apply maximum likelihood estimation and the expectation-maximization algorithm to uncover hidden structures in data.
- Employing Network-Based Models for Unsupervised Learning (k4)
Students are capable of understanding and using neural network-based approaches like auto-associator networks, attractor networks, Boltzmann machines, and Helmholtz machines for modeling data distributions and learning latent representations.
- Analyzing Sequential and Probabilistic Models (k4)
Students can implement hidden Markov models, belief networks, and factor graphs to model sequences, probabilistic relationships, and dependencies in unsupervised data.
- Balancing Model Complexity and Performance (k5)
Students are able to critically evaluate and balance the trade-off between model complexity and performance in unsupervised learning, understanding how to optimize techniques for specific applications.
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(*)Students have in-depth knowledge of unsupervised machine learning principles, covering clustering, projection methods, error models, and neural network-based approaches. They understand the theory and practice of techniques like PCA, ICA, matrix factorization, and probabilistic models (e.g., hidden Markov models and belief networks), enabling them to infer meaningful structures in data without explicit targets.
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