| Students have developed practical skills in applying unsupervised machine learning techniques, gaining hands-on experience with clustering, dimensionality reduction, and probabilistic models. They have learned to implement and evaluate various unsupervised methods, understanding how to identify patterns, optimize models, and analyze complex data without explicit targets.
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- Implementing and Applying Error Models (k4)
Students can work with and implement error models, understanding their role in unsupervised learning for representing and minimizing discrepancies in data modeling.
- Applying Maximum Likelihood and Expectation Maximization (k4)
Students are able to implement maximum likelihood estimation and the expectation-maximization algorithm to discover hidden structures within data and optimize unsupervised learning models.
- Performing Clustering and Projection Techniques (k4)
Students can execute clustering methods, including hierarchical clustering and affinity propagation, and apply projection techniques such as PCA, ICA, and factor analysis to uncover data structures.
- Utilizing Advanced Network-Based Models (k4)
Students are capable of implementing neural network-based models like auto-associator networks, attractor networks, and applying Boltzmann and Helmholtz machines for deep structure discovery in data.
- Analyzing and Using Probabilistic Graphical Models (k4)
Students can practically implement hidden Markov models, belief networks, and factor graphs to model sequences, probabilistic relationships, and latent variable structures in datasets.
- Working with Matrix Factorization and Maximum Entropy Methods (k4)
Students can apply matrix factorization techniques and maximum entropy methods to analyze data distributions, reduce dimensionality, and maximize information retention.
- Balancing Information Bottleneck and Model Optimization (k5)
Students are able to apply the information bottleneck method to maximize the relevant information extracted from data while balancing model complexity and interpretability.
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Students have practical knowledge of key unsupervised learning techniques, covering clustering, dimensionality reduction, error modeling, and probabilistic methods. They know how to apply and evaluate advanced techniques like factorization, neural networks, and graphical models, having gained insight into how to analyze and interpret complex, unstructured data.
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