Intelligent Data Analysis and Decision Support

Detailed topics of the presentations:

1. Estimation and decision theory, optimal decision and properties of human decisions, types of utility functions. Intelligent inference types: probabilistic, causal and counterfactual inference. Value of information and optimal information gathering strategies.
2. Intelligent data analysis methods, data analysis on different types of data (tabular, time series, unstructured).
3. Regression type decision problems. Regularized regression methods: ridge, lasso, elastic net.
4. Non-linear dimension reduction methods (autoencoder, manifold). Applications of dimensionality reduction.
5. Clustering for clustering tasks and as a preprocessing of classification problems. Biclustering, spectral clustering methods.
6. Improving the performance (accuracy) of ML methods. Ensemble (ECOC) machine learning methods.
7. Types of recommender systems and data analysis methods. Matrix factorization and collaborative filtering in recommender systems.
8. Data-driven decision support with machine learning models. Decision evaluation process.
9. Definitions, parametric and structural semantics of probabilistic graphical models, use of sparse representations, inference algorithms, notable classes of models (naive Bayes nets, Hidden Markov Models). Extensions to first-order probabilistic logics and stochastic grammars.
10. Derivation of causal models, notion of observational equivalence. Modelling interventions using do(.) semantics and graph truncation. The notion of correction in causal power estimation. Counterfactual inference.
11. Conjugacy and sufficient statistics in exact Bayesian inference.  Approximation methods for Bayesian inference. Monte Carlo methods, rejection sampling and importance sampling. Markov Chain Monte Carlo Methods (MLMC): convergence and confidence diagnostics, multilinear methods, Metropolis-linked MLMC. Hybrid MLMC. 
12. Learning causal models from observation and intervention data.  Learning with background knowledge, data and knowledge fusion in learning system models. Bayesian learning of model properties.
13. Active learning, learning with cost. k-armed bandits, Monte Carlo tree search. Reinforcement learning, deep reinforcement learning. 
14. Recommender systems, noise and informative miss handling. Discovery systems, early discovery performance measures, expected utility of experiment, adaptive experiment design. 

Detailed topics for exercises:

1. Decision model construction. Optimal decision and value of information.
2. Advanced regression exercise in Python
3. Spectral clustering on images (Python)
4. Joint machine learning methods (computational exercise)
5. Constructing a causal model. Probabilistic, causal and counterfactual inference testing.
6. Examination of Markov Chain Monte Carlo methods: Gibbs and hybrid MCMC sampling.
7. Hyperparameter optimization with k-armed robbers and deep learning Monte Carlo tree search.