Evaluate Data Mining Applications: Critically evaluate the value and application of data mining techniques for addressing real-life problems across various domains.

Comprehensive Understanding of Data Mining Methods: Explore diverse methods in data mining, encompassing data analysis, statistical techniques, machine learning algorithms, and model validation procedures.

Understand and apply fundamental modeling approaches including linear regression, linear classifiers, decision tree models, and clustering algorithms.

Explain the No-Free-Lunch theorems and elucidate the significance of prior knowledge in solving machine learning problems effectively.

Bias-Variance Analysis and Regularization: Derive the bias-variance decomposition for Mean Squared Error (MSE) and "0-1" losses, and illustrate how regularization impacts the bias-variance tradeoff.

Ensemble Learning Techniques: Explain bootstrapping, bagging, and boosting concepts, and justify the selection of weak learners for specific aggregating algorithms.

Relationship Between Linear Models and Deep Neural Networks: Discuss the connection between linear models and deep neural networks, and describe the training process of neural networks.

Understand the principles of Generative Adversarial Networks, identify metrics they optimize, and explore techniques for regularization.

Handling Imbalanced Datasets: Apply techniques for effectively working with imbalanced datasets, ensuring robustness in model training and evaluation.

Knowledge Discovery and Data Mining Tasks: Perform essential computational tasks of data mining such as pattern extraction, association mining, classification, clustering, ranking, prediction, and outlier detection.

Demonstrate the formulation and representation of real-world applications as different types of data, including matrices, itemsets, sequences, time series, data streams, etc.

Identify appropriate data mining techniques for specific real-world scenarios, considering the nature of the data and the problem at hand.

Apply state-of-the-art data mining techniques to address various problems across different domains effectively.

Develop software development skills necessary for handling large-scale datasets, including data preprocessing, model development, and evaluation, considering datasets with millions of records.