Introduction to inference, through the simulation process. Explore probability, exponential families, conditional probabilities and Bayes theorem, inference and Maximum Likelihood estimation, confidence intervals, and hypothesis testing (emphasis on simulation).

Introduction to the basics of regression-based modeling. Explore simple and multiple regression, interpretation of models and coefficients, prediction and estimates, regularization processes, and generalized linear models.

Explore advanced techniques used in practice for regression-based models. Examine time series and longitudinal data, repeated and mixed models, spatially correlated data, and Random Forest models.

Explore the field of "big data" and the rigors of determining applicable design structures from that data. Examine classic design structures, non-typical data structures and novel design processes, and causal inference, and explore data-based decision making.

Examine visualization techniques used in practice to discover insights about data. Explore data quality and relevance, data ethics and providence, clustering, dimension reduction, and reproducibility.

Explore topics in data science with a general overview of statistics theory and application.

Explore computational methods for syntactic and semantic analysis of structures representing meanings of natural language, the study of current natural language processing systems, and methods for computing outlines and discourse structures of descriptive text.

Explore algorithm design and analysis including algorithmic paradigms, maximum flow, randomized algorithms, data structures, NP-completeness and approximation algorithms.

Explore a background on convex sets and functions, linear programming, convex programming, and iterative first-order and second-order methods.

Examine programming skills, including testing, debugging, and the basics of programming methodology. Explore fundamental concepts in data structures and algorithms.

Examine computing systems that automatically improve their performance with experience, including various approaches to inductive classification such as version space, decision tree, rule-based, neural network, Bayesian, and instance-based methods; as well as computational learning theory, explanation-based learning, and knowledge refinement.

Explore the basic building blocks and intuitions behind designing, training, tuning, and monitoring of deep networks. Examine both the theory of deep learning, as well as hands-on implementation sessions in pytorch. Explore a series of application areas of deep networks in: computer vision, sequence modeling in natural language processing, deep reinforcement learning, generative modeling, and adversarial learning.

Introduction to the theory and practice of modern reinforcement learning, with emphasis on temporal difference learning algorithms.

Explore topics in data science with a general overview of computer science application.