Learning Objectives

• Determine degree of homophily, or evidence of structural balance from analysis of large graphs.
• Compute pure and mixed Nash equilibria in a standard form representation of a game.
• Determine auction winners and compute payments in common auction types used in online advertising.
• Given a large directed graph, be able to compute hub and authority scores, as well as PageRank scores.
• Generate random graphs, graphs with specified degree distributions.
• Identify equilibria and the degree of behavior spread in network cascades.
• Compute key network properties including communities from large example graphs.
• Generate graphs that enable efficient decentralized search.

Required & Recommended Text

• Required: D. Easley and J. Kleinberg. Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, 2010.
• Recommended: Supplementary readings — the research papers behind each lesson, grouped by lesson and linked. They are recommended, not required; you are assessed only on the lectures and worksheets.

Assessments

This class focuses on students learning by doing — students will learn key ideas by work on computational worksheets in class and through short exams (held at CBTF) and a long homework programming assignment. Note: late assignments are not accepted; see the late submissions policy below.

Four credits students have the opportunity to dive deeper into the topics discussed in class through developing a functional app related to any topic relevant to the class. For example, build a new social media app focused on say privacy concerns, develop an agentic moderation system, or develop a better social media feed construction algorithm that supports greater user engagement while reducing the spread of AI slop. The expectation is that the prototype is mature and ready to be put into development. Here are two examples of app development projects [App 1], [App 2].

Four-credit students may do the project individually or in a group of at most two. Partners submit identical deliverables with both names on each, and from the mid-semester report on, each deliverable ends with a short statement of who did what. Both partners receive the same project grade, and when we grade the prototype and the evaluation, we take the size of the group into account. Within the project grade, the proposal counts for 5%, the mid-semester report 25%, the final report 35% and the presentation 35%.

Use of Generative AI technologies

Use of generative AI technologies is allowed and encouraged in this class, provided you engage with them critically and transparently. Large language models can serve as valuable thinking partners—helping you explore ideas, debug code, understand complex concepts, or refine your writing—but they cannot replace your own deep engagement with the material. When you use AI tools for course assessments (homework, projects), you must demonstrate that you understand the underlying concepts and can critically evaluate AI-generated content.

Your Obligation: clearly documenting when and how you used AI, verifying any claims or code the AI produces, and being able to explain your reasoning independent of the tool. Think of AI as a collaborator that helps you think more deeply, not a shortcut that thinks for you. Your submissions should reflect your own critical analysis, original insights, and genuine understanding of social and information networks concepts. Tools for grammar, spell-checking, and rephrasing remain welcome as before. The goal is to develop your ability to think causally and statistically—skills that require active reflection, not passive consumption of AI outputs.

Key Events and Dates

• In-class worksheets: Released just after midnight on each day we have a class, and due at 11:59pm one week later — so a worksheet released early on a Wednesday is due at 11:59pm the following Wednesday. After the deadline the worksheet stays open and you may redo it, but not for credit. The four extra-credit worksheets have their own deadlines; see Extra credit opportunities below.
• CBTF Short Exams: Each short exam is taken at the CBTF during the three-day window shown below; you must reserve a seat in advance, and reservations open on the date listed in the last column. Reserve early — seats in the final days of a window fill up.

  Short Exam	Testing window (CBTF)	Reservations open	Duration
  #1	Tue, Sep 15 – Thu, Sep 17, 2026	Thu, Sep 3, 2026	50 min
  #2	Tue, Sep 29 – Thu, Oct 1, 2026	Thu, Sep 17, 2026	50 min
  #3	Tue, Oct 13 – Thu, Oct 15, 2026	Thu, Oct 1, 2026	50 min
  #4	Tue, Oct 27 – Thu, Oct 29, 2026	Thu, Oct 15, 2026	50 min
  #5	Tue, Nov 10 – Thu, Nov 12, 2026	Thu, Oct 29, 2026	50 min
  #6	Tue, Dec 1 – Thu, Dec 3, 2026	Thu, Nov 12, 2026	50 min
  Final Exam	Thu, Dec 10 – Wed, Dec 16, 2026	Mon, Oct 19, 2026	1 hr 50 min

• Long Homework: Released Friday, Sep. 25th, 2026 at 5:00pm. Part A due Friday, Oct. 23rd at 5:00pm; Part B due Friday, Nov. 20th at 5:00pm.
• Final project (4-credit): Project proposal due Friday, Oct. 2nd at 5:00pm; mid-semester report due Friday, Oct. 30th at 5:00pm; final project report and presentation (as a 10 minute video recording) due Wednesday, Dec. 9th, 2026 at 5:00pm, the last day of class.

Expectations on Assignments

• In-class worksheets: Every class meeting has a computational worksheet on PrairieLearn that you work through during class. Worksheets are released just after midnight on the day of the class and are due at 11:59pm one week later — so a worksheet released early on a Wednesday is due at 11:59pm the following Wednesday. Each worksheet has about 30 problems: you begin it during class and have the rest of the week to finish it, submitting at any point in that week. After the deadline the worksheet stays open and you may redo it, but not for credit. You are encouraged to work with your peers and discuss ideas during class, but the work you submit must be your own.
• Short Exam: Every two weeks, students will take a 50-minute short exam covering the previous two weeks' materials. Short exams are held in the CBTF (Computer-Based Testing Facility) at the Urbana and the Chicago locations. You must reserve a seat in advance. Reservations for most short exams open twelve days before the testing window; Short Exam #6 and the final exam open earlier, so check the dates under Key Events and Dates above.
• Long Homework: There will be one long homework assignment, comprising two parts (Part A and Part B, covering the various course topics) with three programming questions each, six programming questions in all. Three-credit students are required to complete the first two questions in each part; four-credit students are required to complete all six. The long homework will be released on Friday, Sep. 25th at 5:00pm; responses to Part A are due four weeks later on Friday, Oct. 23rd at 5:00pm, and responses to Part B are due on Friday, Nov. 20th at 5:00pm. This is an individual assignment. You may use generative AI tools subject to the disclosure requirements above: document when and how you used them, verify anything they produce, and be prepared to explain your reasoning without the tool. Students may request one extra day for either part of the long homework, but not for both — if you take an extra day for Part A, you cannot take one for Part B. Please inform the TAs in case you wish to take an extra day.
• Class participation: Students are expected to attend class and participate in the discussions. Participation is assessed through the in-class worksheets described above. Since the class meets over Zoom across two locations, please keep your camera on so that group work during class is possible.
• Final Exam: The final exam is comprehensive, covering all material discussed in class. It is a 1 hour 50 minute exam taken at the CBTF (Computer-Based Testing Facility) at the Urbana and the Chicago locations during the window listed under Key Events and Dates above. As with the short exams, you must reserve a seat in advance.
• Project proposal (4-credit only): 1 page, with additional space for unlimited references. The project proposal should discuss the problem that you wish to tackle in the project. It should read like an extended abstract, explaining what problem you want to tackle and why it is important/urgent to address. Discuss past approaches to tackling the problem, and provide a sketch of what you plan to do and how you plan to evaluate your solution. In particular, discuss your plan to evaluate your design with users. [Rubric]
• Mid-semester report (4-credit only): 4-5 pages, with additional space for unlimited references. The mid-semester report should contain details of the work done, including completed related work, the preliminary idea for the solution, and a detailed assessment methodology, including baselines to evaluate the proposed idea. [Rubric]
• Final project report (4-credit only): 10-12 pages, with additional space for unlimited references. This final report should be written in a standard ACM conference style format and read like a research paper ready for submission. If you are developing a system, a demonstration of the final working system (i.e. a functional design prototype on Figma) is expected during the final presentation. [Rubric]
• Final presentation (4-credit only): A 10 minute video recording that motivates the problem, walks through the working prototype end to end, reports what user feedback changed and what the evaluation showed. [Rubric]

Grading

Type	Number	Weight (3-credit)	Weight (4-credit)	Location
In-class worksheets	25	10%	7.5%	PrairieLearn
Short Exam (best 5 out of 6)	6	50%	37.5%	CBTF
Long Homework	1	20%	15%	Gradescope
Final Exam	1	20%	15%	CBTF
Final Project (4-credit students): proposal 5%, mid-semester report 25%, final report 35%, presentation 35%	1	—	25%	Gradescope
	Total	100%	100%

Extra credit opportunities

Extra credit is available in three ways. First, the third programming question in each part of the long homework is required for four-credit students; three-credit students may attempt those two questions for up to 3% of extra credit. Second, four extra-credit worksheets are released on the four own time days shown in the course schedule — two for Lesson 5, one for Lesson 12, and one for Lesson 14 on the last day of class. Students who complete all four earn 1% of extra credit. Worksheets #5.1, #5.2 and #12.2 are due Wednesday, Dec. 9th at 5:00pm; Worksheet #14.1, released on the last day of class, is due Wednesday, Dec. 16th at 11:59pm. Third, all students can earn up to 1% of extra credit by completing the teaching survey; this is allocated to everyone in proportion to the fraction of the class that responds, so if half the class completes the survey then all students receive 0.5%, and if everyone completes it all students receive the full 1%.

Class Participation

In this class, we will learn by doing, and an important part of the learning process is to work on the in-class computational worksheets on PrairieLearn, where you will engage with the material in depth. Worksheets are due at 11:59pm one week after they are released. Since the class is taught online across two locations, it is important that all students participate in the class discussions; please keep your camera on during class so that group work is possible.

Grade cutoffs

The grade cutoffs are (lower end of the range is shown for each grade; the upper bound is below the next higher grade):

A+: ≥ 98, A ≥ 90, A- ≥ 85, B+ ≥ 80, B ≥ 75, B- ≥ 70, C+ ≥ 65, C ≥ 60, C- ≥ 55, D ≥ 45, E ≥ 35, F ≤ 35

The scores will be rounded upwards and then the grade will be assigned. So for example, a score of 89.4 would be rounded to 90 and receive an A.

Course Schedule

All class meetings are online over Zoom. Worksheets are completed on PrairieLearn and are due at 11:59pm one week after they are released; after the deadline a worksheet stays open for practice, but not for credit. The four extra-credit worksheets have their own deadlines — see Extra credit opportunities above.

The six CBTF short exams take 300 minutes of your time in total (6 × 50 minutes). That time is given back to you as four own time days (4 × 75 minutes = 300 minutes), marked below: there is no class meeting on those days. The worksheet for each of those days is released as an extra-credit worksheet instead — complete all four for 1% of extra credit (see Extra credit opportunities above).

Class Date	Topic	Lesson	Worksheets	CBTF Short Exam date	Long Homework & Project
Wed, Aug 26, 2026	Introduction	Lesson #0	Worksheet #0
Fri, Aug 28, 2026	ties, triads and holes (chapter 3)	Lesson #1	Worksheet #1.1
Wed, Sep 2, 2026	structural balance (chapter 5)		Worksheet #1.2
Fri, Sep 4, 2026	homophily (chapter 4)		Worksheet #1.3
Wed, Sep 9, 2026	Introduction to Game Theory (chapter 6)	Lesson #2	Worksheet #2.1
Fri, Sep 11, 2026	Game Theory contd. (chapter 6)		Worksheet #2.2
Wed, Sep 16, 2026	Introduction to Auctions (chapter 9)	Lesson #3	Worksheet #3.1	Short Exam #1: Tue, Sep 15 – Thu, Sep 17
Fri, Sep 18, 2026	Auctions contd. (chapter 9)		Worksheet #3.2
Wed, Sep 23, 2026	Matching Markets (chapter 10)	Lesson #4	Worksheet #4.1
Fri, Sep 25, 2026	Matching Markets contd. (chapter 10)		Worksheet #4.2		Long Homework released, 5:00pm
Wed, Sep 30, 2026	No class, own time. Bargaining and Power in Networks (chapter 12)	Lesson #5	Extra credit Worksheet #5.1	Short Exam #2: Tue, Sep 29 – Thu, Oct 1
Fri, Oct 2, 2026	No class, own time. Bargaining and Power contd. (chapter 12)		Extra credit Worksheet #5.2		Project proposal due, 5:00pm (4-credit)
Wed, Oct 7, 2026	Introduction to the Web (chapter 13) and Web search (chapter 14)	Lesson #6	Worksheet #6.1
Fri, Oct 9, 2026	Web search contd. (chapter 14)		Worksheet #6.2
Wed, Oct 14, 2026	Introduction to Sponsored Search Markets (chapter 15)	Lesson #7	Worksheet #7.1	Short Exam #3: Tue, Oct 13 – Thu, Oct 15
Fri, Oct 16, 2026	Sponsored search contd. (chapter 15)		Worksheet #7.2
Wed, Oct 21, 2026	Random Graphs Introduction (Instructor Notes)	Lesson #8	Worksheet #8.1
Fri, Oct 23, 2026	Random Graphs contd. (Instructor Notes)		Worksheet #8.2		Long Homework Part A due, 5:00pm
Wed, Oct 28, 2026	Power Laws (chapter 18)	Lesson #9	Worksheet #9.1	Short Exam #4: Tue, Oct 27 – Thu, Oct 29
Fri, Oct 30, 2026	Power Laws contd. (chapter 18)		Worksheet #9.2		Mid-semester report due, 5:00pm (4-credit)
Wed, Nov 4, 2026	Community Discovery (Instructor Notes)	Lesson #10	Worksheet #10.1
Fri, Nov 6, 2026	Community Discovery contd. (Instructor Notes)		Worksheet #10.2
Wed, Nov 11, 2026	Network Effects Introduction (chapter 17)	Lesson #11	Worksheet #11.1	Short Exam #5: Tue, Nov 10 – Thu, Nov 12
Fri, Nov 13, 2026	Network effects contd. (chapter 17)		Worksheet #11.2
Wed, Nov 18, 2026	Cascading behavior Intro (chapter 19)	Lesson #12	Worksheet #12.1
Fri, Nov 20, 2026	No class, own time. Cascading behavior contd. (chapter 19)		Extra credit Worksheet #12.2		Long Homework Part B due, 5:00pm
11/21—11/29	Fall & Thanksgiving Break
Wed, Dec 2, 2026	Small World Phenomena (chapter 20)	Lesson #13	Worksheet #13.1	Short Exam #6: Tue, Dec 1 – Thu, Dec 3
Fri, Dec 4, 2026	Small World Phenomena contd. (chapter 20)		Worksheet #13.2
Wed, Dec 9, 2026	No class, own time. Markets and Information (chapter 22)	Lesson #14	Extra credit Worksheet #14.1		Final project report and presentation due, 5:00pm (4-credit)

Detailed Learning Outcomes

Class	Topic	Lesson	Broad Learning Goals	Learning Outcome #1	Learning Outcome #2	Learning Outcome #3
Wed, Aug 26, 2026	Introduction	Lesson #0
Fri, Aug 28, 2026	ties, triads and holes (chapter 3)	Lesson #1	Be able to appreciate: the role of ties in information diffusion; how tie asymmetry affects persuasion Understand how tie strength is manifest in observed behavior on Social and Communication Networks	Students will identify nodes in example graphs that violate strong triadic closure property	Students will be able to compute node properties including node clustering coefficients as well as identify essential graph elements including bridges and local bridges in graphs	Students will be able to prove that local bridges attached to nodes that satisfy the strong triadic closure property in a graph must be weak ties
Wed, Sep 2, 2026	structural balance (chapter 5)		Understand that structural balance is both a local and a global property Understand evolution of structural balance Understand how to extend structural balance theory to non-complete graphs	Students will identify triangles in example graphs that violate structural balance	Students will be able to prove the structural balance theorem	Students will analyze non-complete, signed graphs to determine if they are balanced
Fri, Sep 4, 2026	homophily (chapter 4)		Understand homophily and mechanisms for its existence. Selection and Socialization mechanisms are generically confounded in observational social network data Spatial segregation can arise out of weak preferences.	Students will change the degree of homophily in example graphs, by adding or removing edges and vertices	Students will be able to prove that we cannot dis-entangle homophily from influence in observational data.	Students will apply Schelling’s model with different mobility thresholds to example spatial graphs and evaluate degree of segregation.
Wed, Sep 9, 2026	Introduction to Game Theory (chapter 6)	Lesson #2	Be able to identify essential elements of Game Theory Understand the differences between dominant strategies, best-responses and Nash equilibria Understand the different kinds of welfare	Students will identify best-responses, dominant strategies in example payoff matrixes	Students will determine pure or mixed Nash equilibria in example payoff matrixes	Students will identify strategies that are either Pareto-optimal or which maximize social welfare in example payoff matrixes
Fri, Sep 11, 2026	Game Theory contd. (chapter 6)
Wed, Sep 16, 2026	Introduction to Auctions (chapter 9)	Lesson #3	Understand the differences and equivalencies between different auction types Understand differences between mechanisms and games Know if an auction has a dominant strategy	Students will be able to prove that bidding true value is the dominant strategy in a second priced auction	Students will be able to show that degree of shading in first-price auctions depends on the number of participants	Students will be able to determine optimal reserve price in a second priced auction
Fri, Sep 18, 2026	Auctions contd. (chapter 9)
Wed, Sep 23, 2026	Matching Markets (chapter 10)	Lesson #4	Be able to use bi-partite graphs to represent a buyer-seller market Know the central role of constricted sets in preventing maximal matching Understand the importance of matching in social welfare	Students will be able to identify constricted sets in example graphs	Students will be able to construct that a preferred seller graph given buyer valuations and seller prices.	Students will be able to construct market clearing prices in an example buyer-seller graph.
Fri, Sep 25, 2026	Matching Markets contd. (chapter 10)
Wed, Sep 30, 2026	Bargaining and Power in Networks (chapter 12) (own time, no class)	Lesson #5	Understand that power-asymmetries may arise out of network structure Understand the importance of an outside option in bargaining	Students will be able to identify nodes in simple graph that exhibit limited, weak or strong power	Students will be able to determine the bargaining outcome across an edge using Nash Bargaining Solution.
Fri, Oct 2, 2026	Bargaining and Power contd. (chapter 12) (own time, no class)
Wed, Oct 7, 2026	Introduction to the Web (chapter 13) and Web search (chapter 14)	Lesson #6	Know that the Web is a large directed graph, with several strongly connected components Understand that searching the Web is difficult because of: polysemy\sidenote{same word has different meanings}; synonymy\sidenote{different words mean the same thing}; the large number of authors; and the dynamic nature of web content. The key idea behind web search is recursive: a web page's importance lies in its ability to point to useful web pages, and that a page's utility depends on the number of important pages that point to it.	Students will be able to identify strongly connected components in example directed graphs	Students will be able to compute the hub and authority scores in example directed graphs by applying the HITS algorithm.	Students will be able to compute the PageRank scores in example directed graphs by applying the PageRank algorithm.
Fri, Oct 9, 2026	Web search contd. (chapter 14)
Wed, Oct 14, 2026	Introduction to Sponsored Search Markets (chapter 15)	Lesson #7	Know the role of Sponsored search advertising in facilitating free access to services, including search, and social media. Understand the importance of auctions for efficiently allocating advertising slots to prospective advertisers. Know that VCG auctions are incentive-compatible, but widely used GSP auctions are not.	Students will be able to compute costs of each ad slot in an example auction.	Students will be able to compute the harm that a participant causes by participating in a VCG auction.	Students will be able to identify the assignments of slots to advertisers in a GSP auction.
Fri, Oct 16, 2026	Sponsored search contd. (chapter 15)
Wed, Oct 21, 2026	Random Graphs Introduction (Instructor Notes)	Lesson #8	Know how Erdös-Réyni (E-R) family of random graphs are parameterized. Know different properties to characterize Erdös-Réyni family of random graphs ; clustering coefficient; degree distribution; diameter. Know that Erdös-Réyni family of random graphs undergo phase transitions resulting in a giant component.	Students will be able to compute the degree distribution of an Erdös-Réyni graph when $np = \lambda$, for large $n$.	Students will be able to show that the diameter of an Erdös-Réyni graph is $O(\frac{\log n}{\log z})$, where $z=(n-1)p$.	By examining example graphs, students will be able to reason about the value of $p$ in relation to the phase transition point probability.
Fri, Oct 23, 2026	Random Graphs contd. (Instructor Notes)
Wed, Oct 28, 2026	Power Laws (chapter 18)	Lesson #9	Know how large scale networks exhibit Power-law like distributions and are not well described by Erdös-Réyni family of random graphs. Power-law distributions can emerge from simple preferential attachment (i.e., ``rich get richer'') processes. Know that scaling laws appear in a variety of contexts.	Students will be able to derive basic properties of a Power-law distribution.	Students will be able to compute the exponent of a Power-law distribution given data.	Students will be able to show the preferential attachment process leads to a power-law
Fri, Oct 30, 2026	Power Laws contd. (chapter 18)
Wed, Nov 4, 2026	Community Discovery (Instructor Notes)	Lesson #10	1. Know that community discovery aims to identify groups of individuals consistent with prior work on homophily, triadic-closure and weak ties. 2. That algorithms for discovering communities are part of a larger family of graph partitioning algorithms.	Students will be able to compute the betweenness centrality measure in an undirected graph.	Students will be able to compute the partition quality using modularity.
Fri, Nov 6, 2026	Community Discovery contd. (Instructor Notes)
Wed, Nov 11, 2026	Network Effects Introduction (chapter 17)	Lesson #11	1. Know what externalities are. 2. Intrinsic interest in a good along with population effects, that is multiplicative benefits from a fraction of the population adopting the good, can cause rapid adoption of the good.	Students will be able to identify the market equilibrium when there are no population effects.	Students will be able to compute the market equilibrium when there are intrinsic and population effects.
Fri, Nov 13, 2026	Network effects contd. (chapter 17)
Wed, Nov 18, 2026	Cascading behavior Intro (chapter 19)	Lesson #12	Know the linear threshold model of behavior spread in a social network.	Given payoffs of two behaviors A, B and a graph, and locations of initial adopters of the superior behavior A, students will be able to determine how the behavior A spreads in the graph by applying the linear threshold model.	The students will be able to identify the cascade capacity of an arbitrary graph.	The students will determine how a superior behavior spreads when nodes in a social network can be bi-lingual.
Fri, Nov 20, 2026	Cascading behavior contd. (chapter 19) (own time, no class)
11/21—11/29	Fall & Thanksgiving Break
Wed, Dec 2, 2026	Small World Phenomena (chapter 20)	Lesson #13	Know that short paths are in abundance in social networks and that individuals can efficiently discover them through decentralized search.	Be able to reason the importance of random links in reducing the diameter.	Be able to reason about the presence of attribute that acts as information gradient that allows for efficient myopic search.	Prove that when the probability of random links between nodes 𝑢, 𝑣 encodes distance information as in$p(u,v) \propto d(u,v)^{-q}$, the social network supports efficient myopic search.
Fri, Dec 4, 2026	Small World Phenomena contd. (chapter 20)
Wed, Dec 9, 2026	Markets and Information (chapter 22) (own time, no class)	Lesson #14	1. Know that we can compute accurate aggregates from crowd estimates, provided there is cognitive diversity and independence of observations. 2. Understand the role of signaling in markets with asymmetric information.	Be able to compute the state prices in horse racing and in the stock market.	Be able to reason about the self fulfilling expectations equilibria in markets with asymmetric information.

Course Policies

Please read all the policies below carefully. In particular, quizzes/exam are to be taken on the particular date and assignments are to be submitted on time; late submissions will not be accepted except in the case of documented emergencies or religious absences approved in advance. If you have any questions about the policies, please ask the instructor.

Late Submissions

All assignments are due at the date and time marked for the Assignments. Late submissions will not be accepted except in the case of documented emergencies or religious absences approved in advance. If you have any questions about the policies, please ask the instructor.

Academic Integrity

The University of Illinois at Urbana-Champaign Student Code should also be considered as a part of this syllabus. Students should pay particular attention to Article 1, Part 4: Academic Integrity. Read the Code at the following URL:http://studentcode.illinois.edu/ Also, read the CS honor code here.

Academic dishonesty may result in a failing grade. Every student is expected to review and abide by the Academic Integrity Policy. Ignorance is not an excuse for any academic dishonesty. It is your responsibility to read this policy to avoid any misunderstanding. Do not hesitate to ask the instructor(s) if you are ever in doubt about what constitutes plagiarism, cheating, or any other breach of academic integrity.

Religious Observances

Illinois law requires the University to reasonably accommodate its students' religious beliefs, observances, and practices in regard to admissions, class attendance, and the scheduling of examinations and work requirements. You should examine this syllabus at the beginning of the semester for potential conflicts between course deadlines and any of your religious observances. If a conflict exists, you should notify your instructor of the conflict and request appropriate accommodations. This should be done in the first two weeks of classes.

Other Absences

Students are expected to attend all classes. If you are unable to attend a class due to illness or other serious situation including family emergencies, notify the instructor and please submit an absence letter from the Dean of Students

Statement on CS CARES and CS Values and Code of Conduct

All members of the Siebel School of Computing and Data Science - faculty, staff, and students - are expected to adhere to the CS Values and Code of Conduct. The CS CARES Committee is available to serve as a resource to help people who are concerned about or experience a potential violation of the Code. If you experience such issues, please contact the CS CARES Committee. The instructors of this course are also available for issues related to this class.

Disability-Related Accommodations

To obtain disability-related academic adjustments and/or auxiliary aids, students with disabilities must contact the course instructor and the Disability Resources and Educational Services (DRES) as soon as possible. To contact DRES, you may visit 1207 S. Oak St., Champaign, call 333-4603, email or go to this URL. If you are concerned you have a disability-related condition that is impacting your academic progress, there are academic screening appointments available that can help diagnosis a previously undiagnosed disability. You may access these by visiting the DRES website and selecting “Request an Academic Screening” at the bottom of the page.

Mental Health

Diminished mental health, including significant stress, mood changes, excessive worry, substance/alcohol abuse, or problems with eating and/or sleeping can interfere with optimal academic performance, social development, and emotional wellbeing. The University of Illinois offers a variety of confidential services including individual and group counseling, crisis intervention, psychiatric services, and specialized screenings at no additional cost. If you or someone you know experiences any of the above mental health concerns, it is strongly encouraged to contact or visit any of the University’s resources provided below. Getting help is a smart and courageous thing to do -- for yourself and for those who care about you.

Counseling Center: 217-333-3704, 610 East John Street Champaign, IL 61820

McKinley Health Center: 217-333-2700, 1109 South Lincoln Avenue Urbana, IL 61801

Sexual Misconduct Reporting Obligation

The University of Illinois is committed to combating sexual misconduct. Faculty and staff members are required to report any instances of sexual misconduct to the University’s Title IX Office. In turn, an individual with the Title IX Office will provide information about rights and options, including accommodations, support services, the campus disciplinary process, and law enforcement options.

A list of the designated University employees who, as counselors, confidential advisors, and medical professionals, do not have this reporting responsibility and can maintain confidentiality, can be found here

Other information about resources and reporting is available here:

Family Educational Rights and Privacy Act (FERPA)

Any student who has suppressed their directory information pursuant to Family Educational Rights and Privacy Act (FERPA) should self-identify to the instructor to ensure protection of the privacy of their attendance in this course. See https://registrar.illinois.edu/academic-records/ferpa/ for more information on FERPA.