IS 597

Spring 2024 Part of Term 1

Part of Term 1
Jan 16-May 1

Advanced Topics in Data Analytics & Data Science

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Credit: 2 OR 4 hours.

Variety of newly developed and advanced topics courses within the fields of Data Analytics & Data Science, intended to augment the existing Information Sciences curricula.

2 or 4 graduate hours. No professional credit. Approved for Letter and S/U grading. May be repeated in the same or separate semesters to a maximum of 16 hours, if topics vary.

This course satisfies the General Education Criteria in Fall 2022 for:

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IS 597 class schedule data for spring 2024
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
73038	Lecture-Discussion	CS	1:00PM -3:50PM	M	46 Grad Sch of Lib & Info Science	LeBlanc, Z	Part of Term: 1 Date Range: 01/16/24-05/01/24 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Culture At Scale: A Seminar Section Info: Other interested students should email the instructor for approval: zleblanc@illinois.edu. How does reading a novel or a dozen, compare to studying hundreds if not thousands? What about paintings or songs? This seminar is devoted to understanding how we can study and produce culture at scale. Bridging theoretical and technical, we will uncover how computation can influence our understandings of culture and how in turn focusing on culture can impact how and when we use computation. This is a HYBRID course that meets with IS 597 CSO. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to PHD:Library & Infor Sci -UIUC, PHD: Informatics - UIUC, or PHD:Information Sciences -UIUC.
72768	Online	CSO	1:00PM -3:50PM	M	n.a.	LeBlanc, Z	Part of Term: 1 Date Range: 01/16/24-05/01/24 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Culture At Scale: A Seminar Section Info: Other interested students should email the instructor for approval: zleblanc@illinois.edu. How does reading a novel or a dozen, compare to studying hundreds if not thousands? What about paintings or songs? This seminar is devoted to understanding how we can study and produce culture at scale. Bridging theoretical and technical, we will uncover how computation can influence our understandings of culture and how in turn focusing on culture can impact how and when we use computation. This is a HYBRID course that meets with IS 597 CS. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to PHD:Library & Infor Sci -UIUC, PHD: Informatics - UIUC, or PHD:Information Sciences -UIUC.
72428	Laboratory-Discussion	DS	9:00AM -11:50AM	F	131 Grad Sch of Lib & Info Science	Weible, J	Part of Term: 1 Date Range: 01/16/24-05/01/24 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Data Structures & Algorithms Section Info: Data Structures & Algorithms via Puzzles & Games. This is an advanced programming and analysis course, requiring effective team coding skills from the start. PREREQUISITES: At least three intermediate-level programming courses including IS597PR, OR contact instructor at jweible@illinois.edu for approval of alternatives. Learn, experiment, code with, and compare performance of common data structures and algorithms in a fun, collaborative, and challenging context. In class, students will solve or play and discuss several types of logic puzzles and strategy games. In small teams they will explore the deductive, strategic, and tactical decisions involved, select appropriate data structures & algorithms to develop efficient program solutions to automate playing, solving, generating, or analyzing puzzles & games. Techniques used include analysis of efficiency (Big-O, Big-theta), recursion, minimax, Monte Carlo Tree Search, client/server network communications, deterministic vs non-deterministic algorithms. Structures used include arrays, hash tables, stacks, various trees, network graphs, and custom structures. For some projects, students will have competitions pitting their solutions against other teams’. Primarily based in the Python language. Intended for IS students who have not formally studied algorithm efficiency & data structures or as a reinforcement of those concepts through applied practice. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to students in the Illinois Informatics Institute or Information Sciences department.
75681	Lecture-Discussion	LLM	9:30AM -12:20PM	R	130 Wohlers Hall	Kilicoglu, H	Part of Term: 1 Date Range: 01/16/24-05/01/24 Degree Notes: ONL Info Science rate course. Special Approval: Instructor Approval Required Credit: 4 hours Section Title: Large Language Models Section Info: Doctoral students and advanced master's students can enroll with the permission of the instructor, email halil@illinois.edu for approval. This seminar-style course is designed to provide a comprehensive exploration of large language models (LLMs) from an information science perspective. It covers the fundamental concepts and algorithms that power LLMs, including deep learning architectures, training strategies, and emerging capabilities, emphasizing core principles. The course also examines the societal impacts of LLMs, considering challenges and risks related to ethics, bias, misinformation, and the potential for transformation in fields such as on healthcare, education, and creative industries. Background in NLP and machine learning basics is helpful (IS 567, IS 577, or similar). Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to students in the Illinois Informatics Institute or Information Sciences department.
75975	Lecture-Discussion	MLC	4:00PM -6:50PM	M	131 Grad Sch of Lib & Info Science	Kim, J Trainor, K	Part of Term: 1 Date Range: 01/16/24-05/01/24 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Machine Learning Cloud Section Info: Developing Machine Learning Pipelines Using Cloud-Based Platforms: This is a graduate-level data science course for students who need practical experience in using machine learning approaches to address real world problems. This course provides hands-on experience in identifying problems suitable for applying machine learning techniques, designing pipeline-based solutions using Python programming, and implementing these solutions on cloud-based platforms. PREREQUISITES: IS430 or equivalent course in Python programming. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to students in the Illinois Informatics Institute or Information Sciences department.
72425	Lecture-Discussion	PR	9:00AM -11:50AM	W	12A Grad Sch of Lib & Info Science	Weible, J	Part of Term: 1 Date Range: 01/16/24-05/01/24 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Progr. & Quality in Analytics Section Info: Programming & Quality in Data Analytics - Prerequisites: Any two previous programming courses or 1 year of experience with any general-purpose language(s). Prior familiarity with Python is very helpful but not required. This is an intermediate-level Python programming course using a broad range of data structures, packages, concepts, best practices, and tools needed for developing, debugging, and modifying software to solve moderately complex problems and to evaluate and improve code maintainability and reliability. These skills are relevant to all contexts of programming, but most scenarios and assignments will include numerical data analysis, Monte Carlo simulation & experimental design, or processing pipelines while using data sets from sciences, finance, business, or government. Introduces test-driven design, OOP features, performance analysis, and concurrent processing. The primary learning objectives are to improve general programming abilities and to develop deeper critical understanding of work in data analytics and common flaws therein. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to students in the Illinois Informatics Institute or Information Sciences department.
75763	Lecture	TML	3:30PM -6:20PM	T	214 Davenport Hall	Wang, H	Part of Term: 1 Date Range: 01/16/24-05/01/24 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Trustworthy Machine Learning Section Info: The course will cover several technical aspects as well as the intuitive understanding of topics under the umbrella of trustworthy machine learning, such as robustness, adversarial robustness, fairness, privacy, and interpretability. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to students in the Illinois Informatics Institute or Information Sciences department.