Course Syllabus
Foundations of Data Analysis
Instructor
Name: El Kindi Rezig
Office: WEB 2815
Email: elkindi@cs.utah.edu
Office Hours: Every Tuesday from 1pm to 3pm
Teaching assistants
Ashley Lujan (ashley.lujan@utah.edu) (Head TA) Office hours: Thursday 1pm-3pm. Location 3145 MEB
Arman Ashkari (u1472216@utah.edu) Office hours: Monday 11am-1pm. Location 3145 MEB
Jaxon Smith (u1455150@utah.edu) Office hours: Friday 12:00pm - 1:00pm Zoom (Passcode: 982332)
Wednesday 4:30pm - 5:30pm. Zoom (Passcode: 982332)
Lucas Pearce (u1110118@utah.edu) Office hours: Friday 12:30pm - 2:30pm. Location 3145 MEB
Zack Freeman (u1190843@utah.edu) Office hours: Wednesday 11am-1pm. Location 3145 MEB
Class Information
Class meetings: Tuesday & Thursday 10:45AM-12:05PM in WEB L101
Piazza: https://piazza.com/utah/fall2025/cs3190001fall2025
Final exam date and location
Thursday, December 11, 2025 10:30 am – 12:30 pm in WEB L101
Class Schedule
| Date | Lecture number | Readings | Notes | |
| 8/19/2025 | Lecture 1 |
Overview of the course and probability review [slides] Probability review [slides] |
Ch 1 - 1.2 from the textbook | Please sign up to Piazza to receive class announcements |
| 8/21/2025 | Lecture 2 | Ch 1 - 1.2 from the textbook | ||
| 8/26/2025 | Lecture 3 |
Probability review [slides][colab] Bayes' Rule [slides] |
Ch 1.3 - 1.6 from the textbook | Homework 1 out |
| 8/28/2025 | Lecture 4 |
Bayes' Rule [slides] Bayes' rule [slides] |
Ch 1.7 from the textbook | |
| 9/2/2025 | Lecture 5 |
Convergence: central limit theorem and concentration measures [slides] |
Ch 1.8 from the textbook | The instructor will be out of town. Prof. Jeff Phillips will cover this class |
| 9/4/2025 | Lecture 6 |
Convergence: central limit theorem and concentration measures [slides] |
Ch 2.1 - 2.2 from the textbook |
Homework 1 due Homework 2 out The instructor will be out of town. Prof. Jeff Phillips will cover this class |
| 9/9/2025 | Lecture 7 |
Convergence: central limit theorem and concentration measures (review) [slides][slides]
|
Ch 2.3 from the textbook | Quiz 1 |
| 9/11/2025 | Lecture 8 |
Linear Algebra review [slides] |
Ch 3.1 - 3.2 from the textbook | |
| 9/16/2025 | Lecture 9 | Ch 3.3 - 3.5 from the textbook | ||
| 9/18/2025 | Lecture 10 |
Linear Regression: explanatory & dependent variables [slides] Linear Regression: multiple regression and polynomial regression [slides] |
Ch 5.1 from the textbook |
Homework 2 due Homework 3 out |
| 9/23/2025 | Lecture 11 |
Linear Regression: multiple regression and polynomial regression [slides] [colab: simple regression] Linear Regression: polynomial regression and cross-validation [slides] |
Ch 5.1 from the textbook |
Quiz 2 Covered material: up to lecture 10 |
| 9/25/2025 | Lecture 12 |
Linear Regression: polynomial regression and cross-validation [slides] [colab: multiple regression] [colab: polynomial regression] Linear Regression: cross-validation and learning curves [slides] [colab: cross validation] [colab: k-cross validation] [colab: learning curves] |
Ch 5.2-5.3 from the textbook | |
| 9/30/2025 | Lecture 13 |
Linear Regression: cross-validation and learning curves [slides] [colab: cross validation] [colab: k-cross validation] [colab: learning curves] |
Ch 5.4 from the textbook | |
| 10/2/2025 | Lecture 14 |
Linear Regression (cont.), learning curves [colab], mid-semester review [slides] |
Ch 5 from the textbook | |
| 10/7/2025 |
Fall break (no class) |
|||
| 10/9/2025 |
Fall break (no class) |
|||
| 10/14/2025 | Lecture 15 |
Gradient Descent 1/4: functions, minimum, maximum, convexity & gradients [slides] Gradient Descent 2/4: gradients [slides] |
Ch 6.1 - 6.2 from textbook | Homework 3 due |
| 10/16/2025 | Lecture 16 |
Gradient Descent 2/4: gradients and directional derivatives [slides] Gradient Descent 3/4: gradient descent algorithm [slides][colab] |
Ch 6.3 from textbook |
Homework 4 out |
| 10/21/2025 | Lecture 17 |
Gradient Descent 4/4: fitting model to data, batch gradient descent, and stochastic gradient descent [slides] |
Ch 6.4 from textbook |
Quiz 3 (postponed to 10/23 due to Canvas outage) |
| 10/23/2025 | Lecture 18 |
Gradient Descent 4/4: fitting model to data, batch gradient descent, and stochastic gradient descent [slides] [colab: learning rates] [colab: batch gradient descent] Dimensionality Reduction: project onto a basis 1/4 [slides] |
Ch 6.4 from textbook |
Quiz 3 Quiz 3 covered material: Lecture 11 to lecture 15 |
| 10/28/2026 | Lecture 19 |
Dimensionality Reduction: project onto a basis 1/4 [slides] |
Ch 7.1 - 7.2 from textbook | |
| 10/30/2025 | Lecture 20 |
Dimensionality Reduction: SVD and rank-k approximation [slides] 2/4 Dimensionality Reduction: SVD and rank-k approximation [slides] 3/4 |
Ch 7.2 - 7.3 from textbook | |
| 11/4/2025 | Lecture 21 |
Dimensionality Reduction: SVD and rank-k approximation, PCA, centering 3/4 [slides] [colab: SVD in action] [colab: PCA centering] [colab: PCA on Iris] |
Ch 7.4 from textbook |
Homework 4 due Homework 5 out |
| 11/6/2025 |
Quiz day |
Quiz 4: This quiz will be on Gradient Descent (Chapter 6, Lecture 15 - Lecture 18) | ||
| 11/11/2025 | Lecture 22 |
Dimensionality Reduction: eigndecomposition and power method 4/4 [slides] [colab: power method] [colab: MDS] |
Ch 7.5 - 7.6 from textbook | |
| 11/13/2025 | Lecture 23 |
Clustering: Voronoi Diagrams + Assignment-based Clustering [slides] Clustering: k-means [slides] |
Ch 8.1 from textbook Ch 8.3 from textbook |
|
| 11/18/2025 | Lecture 24 | Ch 8.4 from textbook |
Quiz 5: Dimensionality reduction (Lecture 19 - Lecture 23) Homework 5 due |
|
| 11/20/2025 | Lecture 25 |
Clustering: EM, Mixture of Gaussians [slides] Classification: Linear prediction [slides] |
Ch 8.4 from textbook Ch 9.1 from textbook |
Homework 6 out |
| 11/25/2025 | Lecture 26 |
Classification: Linear prediction [slides]
|
Ch 9.1 from textbook |
|
| 11/27/2025 |
Thanksgiving (no class) |
|||
| 12/2/2025 | Lecture 27 |
Classification: Perceptron algorithm [slides] Classification: Kernels [slides] End of class: slide 21
|
Ch 9.2 from textbook Ch 9.3 from textbook |
Quiz 6: Clustering, Dimensionality Reduction, and Classification (Lecture 23 to Lecture 26) |
| 12/4/2025 |
Semester review [slides] |
Notes from practice exam solution sketch Homework 6 due |
||
| 12/11/2025 |
Final exam |
External resources
Learn Python for beginners: https://www.learnpython.org
Linear Algebra Review: https://cs229.stanford.edu/section/cs229-linalg.pdf
Probability theory review: https://cs229.stanford.edu/section/cs229-prob.pdf
LaTeX tutorial: https://www.overleaf.com/learn/latex/Tutorials
Video: Andrew Ng's Gradient Descent Lecture: https://www.youtube.com/watch?v=4b4MUYve_U8
Video: PCA: Main ideas presented visually: https://www.youtube.com/watch?v=FgakZw6K1QQ
Paper: PCA tutorial: https://www.cs.cmu.edu/~elaw/papers/pca.pdf
Andrew Ng's Gradient Descent lecture: https://www.youtube.com/watch?v=4b4MUYve_U8
PCA: Main ideas presented visually: https://www.youtube.com/watch?v=FgakZw6K1QQ
PCA tutorial: https://www.cs.cmu.edu/~elaw/papers/pca.pdf
Course Description
Today’s smart applications are powered by various machine learning and AI models that make careful decisions to learn from data and infer insights from it. In this course, we will learn about the
mathematical foundations that underpin modern AI/ML applications. Understanding those concepts is crucial to building effective AI/ML tools and extracting insights from large and often messy
datasets. This course is a gateway to more advanced ML classes. The course will review probability, statistics, Baye’s theorem and its applications, linear algebra, high-dimensional data clustering,
classification, and regression.
Class communication
We will be using Piazza to post announcements and class updates.
Course Objectives
Upon the completion of this course, students should be able to:
- Understand how to use mathematical operations to manipulate data points in the vector
space. - Write simple Python functions using libraries like numpy and scikit-learn to train regression
and classification models. - Understand the intuition behind Baye’s theorem and how to apply Bayesian inference.
- Understand how to use dimensionality reduction techniques to an input dataset.
- Implement popular data clustering methods.
- Optimize models’ parameters using the Gradient Descent algorithm.
- Understand how to fit a model to a data distribution.
- Evaluate models on their ability to generalize to new data.
Textbook
Mathematical Foundations for Data Analysis by Jeff M. Phillips: http://mathfordata.github.io, PDF
link: https://mathfordata.github.io/versions/M4D-v0.6.pdf
Getting help
Students are encouraged to take advantage of the office hours provided by the instructor and TAs.
Students are also encouraged to form discussion groups to study the material with their peers, but
not share answers to problems. Lastly, students can post questions on the Piazza discussion group
of this class. Such questions can relate to the course material, homework, class logistics, etc.
Pre-requisites
CS 2100 (Discrete Structures), CS 2420 (Intro. Alg. & Data Struct), and MATH 2270 (Linear
Algebra). We will go over a review of key concepts in probabilities and linear algebra. However,
students are expected to have taken the pre-requisite classes and possess basic mathematical knowl-
edge.
Grading
The course grade is determined by the following components:
Final exam 20%
Homework 60%
Quizzes 18%
In-class participation 2%
There will be 6 quizzes, in total worth 18% of the grade. We will be using Canvas to conduct those quizzes at the end of select classes.
Quizzes will have questions in the following format: (1) multiple-choice; (2) questions where you have to type in the answer in a text box; and (3) True/False questions. Quizzes have to be taken in class; otherwise, a grade of 0 will be given.
The lowest score in quizzes and homework will be dropped as long as an attempt is made.
In-class participation
In an effort to make sure every student feels they have the opportunity to participate (and earn
participation credit), participation points are earned through various ways:
- Raising hand during class to ask/answer questions
- Polling: I will ask questions during class using PollEverywhere. This way, everyone can answer (using their phone/tablet/laptop). Polling would count towards participation.
Students will need a minimum of 25 participation points (in any of the forms above) to get the full 2% participation credit. Doing more (might) earn you extra credit.
Grade scale
Final grades will be assigned according to the following scale:
• 90-100 : A- to A
• 80-90 : B- to B+
• 70-80 : C- to C+
• 60-70 : D- to D+
• below 60 : E
The instructor reserves the right to adjust those intervals.
Regarding policy
If you believe there is an error in grading an assignment, you may request a regrading within two weeks of receiving your grade. Requests must be made by email to the instructor and Cc’ing all the TAs, explaining clearly why you think your solution is correct. You may consult with the instructor/TA first, but the formal request must always be made by email.
Students requesting a regrade of an assignment need to send an email to the instructor and Cc all the TAs. Students have 2 weeks to contest their assignment grade.
Late Policy
To get full credit for an assignment, it must be turned in through Canvas by the 10 minutes before the end of the day it is due, specifically 11:50pm. Once the 11:50pm deadline is missed, those turned in late will lose 10 points. Every subsequent 24 hours until it is turned, another 10 points is deducted. No assignment is accepted after 3 late days. The late policy does not apply to the final project deadlines.
Collaboration Policy
Students may discuss homework problems with each other and possible solutions in general terms. However, you may not share/get any details of the solutions with/from other students. Shar- ing/copying solution details is considered a violation of the University of Utah Student Code.
Students may post homework discussions on the Piazza discussion group without sharing solutions to these problems. The goal of those discussions is to improve the student’s understanding of the problems so they can solve them themselves.
Academic Honesty, Plagiarism and Cheating
It is assumed that all work submitted to your instructor is your own work. When you have used the ideas of others, you must properly indicate that you have done so.
It is expected that students adhere to University of Utah policies regarding academic honesty, including but not limited to refraining from cheating, plagiarizing, misrepresenting one’s work, and/or inappropriately collaborating. This includes the use of generative artificial intelligence (AI) tools without citation, documentation, or authorization. Students are expected to adhere to the prescribed professional and ethical standards of the profession/discipline for which they are preparing. Any student who engages in academic dishonesty or who violates the professional and ethical standards for their profession/discipline may be subject to academic sanctions as per the University of Utah’s Student Code: https://regulations.utah.edu/academics/6-410.php.
Plagiarism and cheating are serious offenses and may be punished by failure on an individual assignment, and/or failure in the course. Academic misconduct, according to the University of Utah Student Code,
"...Includes, but is not limited to, cheating, misrepresenting one’s work, inappropriately collaborating, plagiarism, and fabrication or falsification of information. . . It also includes facilitating academic misconduct by intentionally helping or attempting to help another to commit an act of academic misconduct.”
For details on plagiarism and other important course conduct issues, see the U’s Code of Student Rights and Responsibilities http://www.regulations.utah.edu/academics/6-400.html.
Additionally, refer to the School of Computing cheating policy here: https://www.cs.utah.edu/docs/misc/cheating_policy.pdf.
Accommodations
Accommodations will be considered on an individual basis and may require documentation. If you need an accommodation for a particular assignment (e.g., quiz), make sure the request is made at least a week before the assignment. Requests made after an assignment deadline has passed will not be considered.
Please contact your instructor as soon as possible to request accommodations of any kind.
All written information in this course can be made available in an alternative format with prior notification to the Center for Disability Services (CDS).
CDS will work with you and the instructor to make arrangements for accommodations. Prior notice is appreciated. To read the full accommodations policy for the University of Utah, please see Section Q of the Instruction & Evaluation regulations http://regulations.utah.edu/academics/6-100.phpLinks to an external site.
If you will need accommodations in this class, contact the Center for Disability Services (801- 581-5020) at disability.utah.eduLinks to an external site.. Address: 162 Union Building, 200 S. Central Campus Dr., Salt Lake City, UT 84112
Safety
The University of Utah values the safety of all campus community members. You will receive important emergency alerts and safety messages regarding campus safety via text message.
For more safety information and to view available training resources, including helpful videos, visit safeu.utah.edu
To report suspicious activity or to request a courtesy escort, contact: Campus Police & Department of Public Safety: 801-585-COPS (801-585-2677) dps.utah.eduLinks to an external site.. Address: 1735 E. S. Campus Dr., Salt Lake City, UT 84112.
Sexual misconduct
Title IX makes it clear that violence and harassment based on sex and gender (which includes sexual orientation and gender identity/expression) is a civil rights offense subject to the same kinds of accountability and the same kinds of support applied to offenses against other protected categories such as race, national origin, color, religion, age, status as a person with a disability, veteran’s status, or genetic information.
If you or someone you know has been harassed or assaulted, you are encouraged to report it to university officials:
Title IX Coordinator & Office of Equal Opportunity and Affirmative Action 801-581-8365
oeo.utah.eduLinks to an external site.
135 Park Building
201 Presidents’ Cir.
Salt Lake City, UT 84112
Office of the Dean of Students 801-581-7066 deanofstudents.utah.edu
270 Union Building
200 S. Central Campus Dr. Salt Lake City, UT 84112
To file a police report, contact:
Campus Police & Department of Public Safety 801-585-COPS (801-585-2677)
dps.utah.eduLinks to an external site.
1735 E. S. Campus Dr.
Salt Lake City, UT 84112
If you do not feel comfortable reporting to authorities, the U’s Victim-Survivor Advocates provide free, confidential, and trauma-informed support services to students, faculty, and staff who have experienced interpersonal violence.
To privately explore options and resources available to you with an advocate, contact: Center for Student Wellness
801-581-7776
wellness.utah.eduLinks to an external site.
328 Student Services Building 201 S. 1460 E.
Salt Lake City, UT 84112