MS in Applied Data Analytics, AI & Machine Learning Concentration

This course covers the concepts of the object-oriented approach to software design and development using Python. It includes a detailed discussion of programming concepts starting with the fundamentals of data types, control structures methods, classes, arrays and strings, and proceeding to advanced topics such as inheritance and polymorphism, creating user interfaces, exceptions and streams. Upon completion of this course students will be able to apply software engineering principles to design and implement Python applications that can be used in with analytics and big data. Effective Fall 2021, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Creativity/Innovation, Critical Thinking. Prerequisite: Programming experience in any language. Or Instructor's consent.  [ 4 cr. ]

This course covers and relates fundamental components of programs. Students use various data structures to solve computational problems, and implement data structures using a high-level programming language. Algorithms are created, decomposed, and expressed as pseudocode. The running time of various algorithms and their computational complexity are analyzed. Prerequisite: MET CS300 and either MET CS520 or MET CS521, or instructor's consent.  [ 4 cr. ]

Formerly titled CS 555 Data Analysis and Visualization with R. This course provides an overview of the statistical tools most commonly used to process, analyze, and visualize data. Topics include simple linear regression, multiple regression, logistic regression, analysis of variance, and survival analysis. These topics are explored using the statistical package R, with a focus on understanding how to use and interpret output from this software as well as how to visualize results. In each topic area, the methodology, including underlying assumptions and the mechanics of how it all works along with appropriate interpretation of the results, are discussed. Concepts are presented in context of real world examples. Recommended Prerequisite: METCS 544 or equivalent knowledge, or instructor's consent.  [ 4 cr. ]

Prerequisite: MET CS 521 or equivalent. Or, instructor's consent. Students will learn major Python tools and techniques for data analysis. There are weekly assignments and mini projects on topics covered in class. These assignments will help build necessary statistical, visualization and other data science skills for effective use of data science in a variety of applications including finance, text processing, time series analysis and recommendation systems. In addition, students will choose a topic for a final project and present it on the last day of class.  [ 4 cr. ]

Formerly titled CS 544 Foundations of Analytics with R. The goal of this course is to provide students with the mathematical and practical background required in the field of data analytics. Probability and statistics concepts will be reviewed as well as the R tool for statistical computing and graphics. Different types of data are investigated along with data summarization techniques and plotting. Data populations using discrete, continuous, and multivariate distributions are explored. Errors during measurements and computations are analyzed in the course. Confidence intervals and hypothesis testing topics are also examined. The concepts covered in the course are demonstrated using R. Laboratory Course. Prereq: METCS 546 and (METCS 520 or METCS 521), or equivalent knowledge, or instructor's consent.  [ 4 cr. ]

Undergraduate Prerequisites: Basic knowledge of Python or R; or consent of instructor. - Mathematics is fundamental to data science and machine learning. In this course, you will review essential mathematical concepts and fundamental procedures illustrated by Python and/or R code and visualizations. Computational methods for data science presented through accessible, self-contained examples, intuitive explanations, and visualization will be discussed. Equal emphasis will be placed on both mathematics and computational methods that are at the heart of many algorithms for data analysis and machine learning. You will also advance your mathematical proficiency enabling you to effectively apply your skills to data analytics and machine learning.  [ 4 cr. ]

Prerequisites: MET CS 544, or MET CS 555 or equivalent knowledge, or instructor's consent. - The Web Mining and Graph Analytics course covers the areas of web mining, machine learning fundamentals, text mining, clustering, and graph analytics. This includes learning fundamentals of machine learning algorithms, how to evaluate algorithm performance, feature engineering, content extraction, sentiment analysis, distance metrics, fundamentals of clustering algorithms, how to evaluate clustering performance, and fundamentals of graph analysis algorithms, link analysis and community detection based on graphs. Laboratory Course.  [ 4 cr. ]

Prerequisites: MET CS 521 & MET CS 546; MET CS 579 or MET CS 669; or consent of instructor. - Study basic concepts and techniques of data mining. Topics include data preparation, classification, performance evaluation, association rule mining, regression and clustering. Students learn underlying theories of data mining algorithms in the class and they practice those algorithms through assignments and a semester-long class project using R. After finishing this course, students will be able to independently perform data mining tasks to solve real-world problems.  [ 4 cr. ]

Graduate Prerequisites: MET CS 248 and MET CS 341 or MET CS 342. - Study of the ideas and techniques that enable computers to behave intelligently. Search, constraint propagations, and reasoning. Knowledge representation, natural language, learning, question answering, inference, visual perception, and/or problem solving. Laboratory course. Prereq: MET CS 341, MET CS 342, MET CS 520 or MET CS 521. Or instructor's consent.  [ 4 cr. ]

Perquisites: MET CS 677 or instructor's consent. - This course focuses on reinforcement learning, covering fundamental concepts and advanced techniques. It begins with an introduction to reinforcement learning and key concepts, such as exploitation versus exploration and Markov Decision Processes. As the course progresses, it delves into state transition diagrams, the Bellman equation, and solutions to the Multi-Armed Bandits problem. Students will explore challenges and methods related to control and prediction. Then, they learn tabular methods, including Monte Carlo, Dynamic Programming, Temporal Difference Learning, SARSA, and Q-Learning. Afterwards, the course also extends into reviewing neural network concepts, covering convolutional and recurrent neural networks, and moves on to approximation methods for both discrete and continuous spaces, including DQN and its variants. Policy gradient methods, actor-critic methods. Finally, ethical considerations in AI and safety issues are also discussed.  [ 4 cr. ]

Prerequisites: MET CS 521; MET CS 622, MET CS 673 or MET CS 682; MET CS 677 strongly recommended; or consent of instructor. Theories and methods for learning from data. The course covers a variety of approaches, including Supervised and Unsupervised Learning, Regression, k-means, KNN's, Neural Nets and Deep Learning, Transformers, Recurrent Neural Nets, Adversarial Learning, Bayesian Learning, and Genetic Algorithms. The underpinnings are covered: perceptrons, backpropagation, attention, and transformers. Each student creates a term project.  [ 4 cr. ]

Prerequisite: MET CS 677 or consent of instructor. Explore the critical intersection between Artificial Intelligence (AI) and cybersecurity, focusing on two interconnected themes: protecting AI systems from vulnerabilities and harnessing the power of AI to address cybersecurity challenges. As AI becomes a cornerstone of modern technology, ensuring the security of AI-powered systems against adversarial attacks, backdoor threats, and model theft is essential. Simultaneously, AI offers transformative capabilities for malware detection, intrusion prevention, and malware analysis. Through a blend of theoretical foundations, hands-on exercises, and real-world case studies, you will study topics such as adversarial machine learning, backdoor injection and defense, intellectual property (IP) protection, and privacy-preserving AI. You will also learn how to design and implement AI-driven tools to identify and mitigate cyber threats in dynamic environments. Practical applications emphasize building resilient AI systems and utilizing advanced AI techniques to enhance security and detect emerging threats. Hands-on labs using existing tools will also be provided and required.  [ 4 cr. ]

Prerequisites: MET CS 677, Python programming, mathematics required for machine learning, and familiarity with neural networks. Or instructor's consent. - The first part of the course covers statistical concepts required for generative artificial intelligence. We review regressions and optimization methods as well as traditional neural network architectures, including perceptron and multilayer perceptron. Next, we move to Convolutional Neural Networks and Recurrent Neural Networks and close this part with Attention and Transformers. The second part of the course focuses on generative neural networks. We start with traditional self-supervised learning algorithms (Self Organized Map and Restricted Boltzmann Machine), then explore Auto Encoder architectures and Generative Adversarial Networks and move toward architectures that construct generative models, including recent advances in NLP, including LLMs, and Retrieval Augmented Methods. Finally, we describe the Neural Radiance Field, 3D Gaussian Splatting, and text-2-image models.  [ 4 cr. ]

Prerequisites: MET CS 566 or instructor's consent. - Students enrolled in this course will gain comprehensive insights into fundamental and advanced concepts within the dynamic realm of computer vision. The curriculum will focus on cutting-edge applications of deep neural networks in computer vision. Through hands-on experiences and practical exercises, students will learn to leverage computer vision and machine learning techniques to solve real-world challenges. This course not only equips students with theoretical knowledge but empowers them to apply these concepts effectively, fostering a deep understanding of how computer vision can be harnessed to address complex problems in diverse industries.  [ 4 cr. ]

This thesis must be completed within 12 months. Students majoring in Computer Science may elect a thesis option. This option is available to Master of Science in Computer Science candidates who have completed at least seven courses toward their degree and have a GPA of 3.7 or higher. Students are responsible for finding a thesis advisor and a principal reader within the department. The advisor must be a full-time faculty member; the principal reader may be part-time faculty member with a doctorate. Permission must be obtained by the department. 4cr.  [ 4 cr. ]

This thesis must be completed within 12 months. Students majoring in Computer Science may elect a thesis option. This option is available to Master of Science in Computer Science candidates who have completed at least seven courses toward their degree and have a GPA of 3.7 or higher. Students are responsible for finding a thesis advisor and a principal reader within the department. The advisor must be a full-time faculty member; the principal reader may be part-time faculty member with a doctorate. Permission must be obtained by the department. 4cr.  [ 4 cr. ]