CS5331 Special Problems in Computer Science: Security: Adversarial Machine Learning
An introduction to adversarial attacks on Machine Learning (ML) that will focus on recent advances in the principles of the attacks, their effects, and possible defense strategies. A specific emphasis will be on recent advances in attacks on deep learning models, due to their prevalence in modern machine learning applications. It is designed to be practical and covers as much as theory as possible. Some specific objectives include:
Outline the different categories of adversarial attacks against machine learning models.
Describe common defense approaches against adversarial attacks for improved robustness of machine learning models.
Understand the basics of adversarial privacy attacks and privacy-preserving defense methods.
CS 5368 Intelligent Systems
Fall 2023, Fall 2022, Fall 2021
This course is an introduction to Artificial Intelligence (AI) that will focus on how to build systems that think and act like humans or rationally on some absolute scale. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm. Topics covered include problem-solving via search, game playing, logical and probabilistic reasoning, planning, Markov modeling, and machine learning (neural nets, reinforcement learning, and genetic algorithms).
The course can be considered as a blend of "classic AI" (still relevant), "statistical AI," and machine learning. Classic AI topic includes Heuristic search and adversarial search. Statistical AI includes probabilistic reasoning, i.e., Bayesian networks, Markov chains, and hidden Markov models. Machine learning includes Naive Bayes, (hopefully perceptron), and neural networks.
By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable, and adversarial settings. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and serve as the foundation for further study in any application area you choose to pursue.
This course is a series of seminars hosted at TTU and given by external speakers. These speakers will be discussing their current research in computer science and related topics.
CS 4354 Concepts of Database Systems
Spring 2025, Spring 2024, Spring 2022
This course provides an overview of a database system and its components, the physical organization of data, data models, relational databases, and query processing.
It has several objectives, including 1) an understanding of data modeling concepts, 2) a relational model for storage and retrieval of information, 3) formal query languages, and 4) current database technologies such as SQL.
This course is an introduction to Artificial Intelligence (AI) that will focus on how to build systems that think and act like humans or rationally on some absolute scale. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm. Topics covered include problem-solving via search, game playing, logical and probabilistic reasoning, planning, Markov modeling, and machine learning (neural nets, reinforcement learning, and genetic algorithms).
The course can be considered as a blend of "classic AI" (still relevant), "statistical AI," and machine learning. Classic AI topics include heuristic search and adversarial search. Statistical AI includes probabilistic reasoning, i.e., Bayesian networks, Markov chains, and hidden Markov models. Machine learning includes Naive Bayes, (hopefully perceptron), and neural networks.