13 courses with the subject DSCI, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
DSCI 351Recommender Systems3.0
Recommender systems are electronic commerce information filtering systems to predict items that the users may have interest in. The goal of this course is to provide an overview of recommender systems, including content-based and collaborative algorithms for recommendation, programming of recommender systems, and evaluation and metrics for recommender systems. The course introduces all relevant topics of Recommender Systems: overview, non-personalized recommendation, content-based recommending, neighborhood-based collaborative filtering, recommender system evaluation and advanced topics. Students will gain hands-on experiences with assignments and a term project.
The goals of this course are to introduce basic theory of deep learning in data science applications, to understand how deep learning algorithms work at a high level, and to apply deep learning algorithms to key data science problems in different disciplines. The course introduces all relevant topics in deep learning: neural networks, backpropagation, convolution neural networks, recurrent neural networks and deep reinforcement learning. Students will be exposed to various representative algorithms in the concept level and learn their trade-offs. Students will gain hands-on experiences with assignments and a term project. Students will be prepared to attack new problems using various deep learning methods.
DSCI 501Quantitative Foundations of Data Science3.0
Linear algebra, calculus, probability and statistical methods are essential foundation areas required for an effective understanding and application of data science. In this course, students will get a gentle introduction to these important areas of quantitative reasoning. Along with introducing basics of linear algebra, calculus, probability, and statistical methods, this course will also introduce their computational application through the Python programming language. Concepts will be demonstrated using various python packages.
Introduces the breadth of data science through a project lifecycle perspective. Covers early-stage data-life cycle activities in depth for the development and dissemination of data sets. Provides technical experience with data harvesting, acquisition, pre-processing, and curation. Concludes with an open-ended term project where students explore data availability, scale, variability, and reliability.
Introduces methods for data analysis and their quantitative foundations in application to pre-processed data. Covers reproducibility and interpretation for project life cycle activities, including data exploration, hypothesis generation and testing, pattern recognition, and task automation. Provides experience with analysis methods for data science from a variety of quantitative disciplines. Concludes with an open-ended term project focused on the application of data exploration and analysis methods with interpretation via statistical, algorithmic, and mathematical reasoning.
Explores data science in practice as an open-ended team activity. Initiates an in-depth multi-term capstone study applying computing and informatics knowledge in a data science project. Teams work to develop a significant product with advisors from industry and/or academia. Explores data science-related issues and challenges involved in the application domain of the team’s choice. Applies a development process structure for project planning, specification, design, implementation, evaluation, and documentation. This course should be taken towards the end of a student's program.
Explores data science in practice as an open-ended team activity. Completes an in-depth multi-term capstone study applying computing and informatics knowledge in a data science project. Teams work to develop a significant product with advisors from industry and/or academia. Explores data science-related issues and challenges involved in the application domain of the team’s choice. Applies a development process structure for project planning, specification, design, implementation, evaluation, and documentation. This course should be taken toward the end of the student's program.
Teaches reproducible, adaptable automation for data processing, analytics, and modeling lifecycles for both scientific research and practical data science and predictive analytics in business settings. Covers data management and workflow automation tools, tracking and versioning data and analytics code, collaboration, integrating with data sources and machine learning deployments, inference servers, and experiment records. Course project applies the principles in an end-to-end data-intensive exercise.
DSCI 631Applied Machine Learning for Data Science3.0
Introduces relevant topics in the life cycle of machine learning: extracting and engineering features, tuning parameters, comparing algorithms, interpreting results, and analyzing errors. Students will be exposed to various representative algorithms in the concept level and learn their trade-offs. Students will gain hands-on experiences with assignments and a term project. Students will be prepared to attack new problems using various machine learning methods and be able to compare the performance of different algorithms for the term project.
The course will cover different technologies in cloud computing. This course focuses on the frameworks and algorithms used in the distributed processing of massive datasets. It will explore both batch and streaming data processing and examine the theory behind their algorithmic approaches. Students will gain practical experience by using cloud computing to solve real world problems.
Recommender systems help users to discover products, information, or other items relevant to their interests, preferences, and current needs. Recommender systems are encountered on multiple domains including e-commerce, content and media distribution, social media, and more. The course will cover fundamental and practical aspects of recommender systems, including data, user and content models, recommendation algorithms, evaluation of recommendation, user aspects, and social impacts. Time is spent on both classical and current techniques and problems in recommendation. Students will gain hands-on experiences with assignments and a term project.
This course offers a comprehensive introduction to the core concepts and recent advancements in natural language processing (NLP) and language modeling. Students will begin with foundational topics such as n-gram models and basic probabilistic approaches, building a strong understanding of the fundamentals of NLP. The course will then progress to state-of-the-art language model architectures, including transformers, GPT-3, and other large language models (LLMs). Students will explore the theoretical foundations of these models, learn about their architecture and training methods, and gain practical experience in building, fine-tuning, and deploying them to address real-world tasks like text understanding, generation, and conversational AI.
DSCI 691Natural Language Processing with Deep Learning3.0
Natural Language Processing (NLP) technologies are among the most important of the information age and form critical components in AI systems. Deep learning approaches predominate the domain, and this course explores the basis of deep architectures for NLP models, placing a strong emphasis on research.
