Bayes, AI, and Deep Learning Foundations of Data Science

1 Probability and Uncertainty
2 Bayes Rule
3 Bayesian Learning
4 Utility, Risk and Decisions
5 A/B Testing
6 Bayesian Hypothesis Testing
7 Stochastic Processes
8 Gaussian Processes
9 Reinforcement Learning
10 Unreasonable Effectiveness of Data
11 Pattern Matching
12 Linear Regression
13 Logistic Regression and Generalized Linear Models
14 Tree Models
15 Forecasting
16 Model Selection
17 Statistical Learning Theory and Regularization
18 Neural Networks
19 Theory of Deep Learning
20 Gradient Descent
21 Quantile Neural Networks
22 Convolutional Neural Networks
23 Natural Language Processing
24 Large Language Models
25 AI Agents

Biography

Vadim Sokolov is Associate Professor in the Department of Systems Engineering and Operations Research at George Mason University, where he develops Bayesian methods, machine learning algorithms, and deep learning architectures for complex systems. His research spans statistical learning theory, probabilistic modeling, and intelligent transportation systems, with publications in leading journals including Bayesian Analysis, Transportation Research, and IEEE Transactions on Intelligent Transportation Systems.

Before joining Mason in 2016, Sokolov served as Visiting Assistant Professor of Statistics at the University of Chicago Booth School of Business and Principal Computational Scientist at Argonne National Laboratory, where he led the development of POLARIS, a large-scale agent-based transportation simulation framework, and the GREET life-cycle analysis model used by over 800 organizations worldwide.

Sokolov earned his Ph.D. in Computational Mathematics from Northern Illinois University and holds a diploma in Applied Mathematics with High Honors from Rostov State University, Russia. His work bridges rigorous statistical foundations with practical applications in energy systems, urban analytics, and data-driven decision-making. He is a member of INFORMS, the International Society for Bayesian Analysis, and the American Statistical Association.

Nicholas Polson is the Robert Law, Jr. Professor of Econometrics and Statistics at the University of Chicago Booth School of Business, where he has shaped modern Bayesian statistics and machine learning since 1991. A leading authority on probabilistic modeling, his research encompasses Markov chain Monte Carlo methods, particle learning, financial econometrics, and deep learning theory, with foundational contributions to stochastic volatility modeling, sparse Bayesian estimation, and high-dimensional inference.

Polson's influential work includes developing particle filtering algorithms for sequential learning and Bayesian regularization methods ranging from Tikhonov to horseshoe priors. His article "Bayesian Analysis of Stochastic Volatility Models" was recognized as one of the most influential papers in the 20th anniversary issue of the Journal of Business and Economic Statistics. He co-authored AIQ: How People and Machines Are Smarter Together (2018), exploring the synergy between human intelligence and artificial intelligence.

Polson earned his master's degree with First Class Honours from Worcester College, Oxford University, and his Ph.D. from the University of Nottingham. His work bridges theoretical foundations in probability and statistics with practical applications in finance, forecasting, and data science, establishing him as a pioneer in connecting classical Bayesian methods to modern deep learning.