Syllabus

Math 408: Advanced Data Analysis

Course Information

Term: Spring 2026

Instructor: Isaac Quintanilla Salinas

Contact: isaac.qs@csuci.edu

Office Location: Marin 2326

Office Hours:

Course Website: m408.inqs.info AND Canvas

Course Description

Students will learn how to construct machine learning models using current data science programming languages. Topics will include nonparametric models, deep learning models, and neural networks. This is a programming intensive course

Learning Outcomes

  • Apply appropriate machine learning techniques given the data set and research goal.
  • Evaluate model performance using standard methods such as cross-validation or simulation studies.
  • Design data workflows to execute machine learning algorithms using standard statistical software.
  • Build machine learning models to be used to predict outcomes from new observations of data.

Required Software

For this course, we will use several different statistical programs to analyze data and construct machine learning models. This course will primarily be taught and support R. However, students may complete assignments and projects in Python. Additionally, students are required to use Quarto, Torch, and Stan in the course. Lastly, students may use any IDE, but the course will only support Positron. All software is available for free. In class, we will download and setup your computing environment on your laptop with the following tools:

  • Quarto is an open source scientific documentation system that allows you to embed code and text in one text file. More information can be found here: quarto.org
    • We will install this in class for R.
    • Comes Installed with Positron and RStudio
  • Stan is a Bayesian analysis software with Hamiltonian Monte Carlo Methods. More information can be found here: mc-stan.org
  • Torch is a set of packages designed to develop and implement neural networks.

Choose One Programming Language

  • R (Recommended) is a statistical package that is available for download at: r-project.org.

  • Python is a general programming langauge that is available to download at: python.org

Choose One IDE

Course Grading

Category Percentage
Assignments 50%
Final Project 25%
Final Presentation 25%

At the end of the quarter, course grades will be assigned according to the following scale:

A+ 98 – 100 B+ 87 – <90 C+ 77 – <80 D+ 67 – <70
A 93 – <98 B 83 – <87 C 73 – <77 D 63 – <67 F < 60
A– 90 - <93 B- 80 – <83 C– 70 – <73 D– 60 – <63

Assignments

Assignments will be assigned on a regular basis and posted here and Canvas. The homework is to help you practice the concepts learned in lecture and to help you study. You must turn in your own individual homework and show your understanding of the material. At the end of the semester, the two lowest homework grades will be dropped. Late work will be accepted, but with a 50% point penalty. The last day late work will be accepted is on DATE.

Final Project and Presentation

Extra Credit

There will be 4 extra credit opportunities worth a total of 10% of your overall grade. (There are no make-ups for missed extra credit assignments!) More information will be provided on the extra credit assignments on a later date. Information on the extra credit can be found here.

Class Schedule

The following outline may be subject to change. Any changes will be announced in class.

Week Topic
1 Intro to Course/Intro to R
2 Bayesian Analysis
3 Bayesian Linear Regression
4 Bayesian Generalized Linear Models
5 Bayesian Zero-Inflated and Hurdle Models
6 Bayesian Survival Analysis
7 Tree-Based Methods & Support Vector Machines
– Spring Break
8 Neural Networks
9 Layers, Optimizers, Loss, and Activation Functions
10 Prediction with Neural Networks
11 Recurrent Neural Networks
12 Recurrent Neural Networks
13 Convolutional Neural Networks
14 Convolutional Neural Networks
15 Final Presentation
16 Final Presentation

Generative Artificial Intelligence Policy

The use of generative artificial intelligence (AI) in an ethical manner is permitted for this course.

Permitted Uses

You may use AI for:

  • Obtain clarification

  • Brainstorming ideas, examples, outlines, and strategies

  • Generating questions for practice or exploration

  • Identifying keywords or phrasing to match professional goals

Prohibited Uses

You may not:

  • Submit AI-generated work

  • Use AI to complete assignments, quizzes, exams, or other assessments meant to reflect your own work

  • Use AI to generate code

Any AI-generated work will receive a 0 in the class. Severe cases will be reported to Academic Misconduct.

You may not upload any course material to any AI platforms such as ChatGPT, Claude, Meta AI, and Google Gemini. Exceptions are allowed for DASS-approved services.

University Policies

Syllabus Policies and Assistance

CSUCI’s Syllabus Policies and Assistance Website provides important details about academic policies, campus expectations, and student support services that are all highly applicable to your success as a student both in and outside of the classroom. Ensure that you review this site on a regular basis to stay informed about the policies and resources that support your success, as campus resources or policies may change semester to semester.

Academic Honesty

Conduct yourself with honesty and integrity. Do not submit others’ work as your own. Foassignments and quizzes that allow you to work with a group, only put your name on what the group submits if you genuinely contributed to the work. Work completely independently on exams, using only the materials that are indicated as allowed. Failure to observe academic honesty results in substantial penalties that can include failing the course.

CSUCI Basic Need

Please use the link to the Basic Needs Program on the Syllabus Policies and Assistance website (<go.csuci.edu/syllabuspolicies>) for information on emergency food, housing accommodations, toiletries, and connections to critical resources.

CSUCI Disability Statement

If you are a student with a disability requesting reasonable accommodations in this course, you need to contact Disability Accommodations and Support Services (DASS) located on the second floor of Arroyo Hall, via email accommodations@csuci.edu or call 805-437-3331. All requests for reasonable accommodations require registration with DASS in advance of need: https://www.csuci.edu/dass/students/apply-for-services.htm. Faculty, students and DASS will work together regarding classroom accommodations. You are encouraged to discuss approved.

Disruption

  1. If I Am Out: I will communicate via email and will hold classes asynchronously.
  2. If You Are Out: Contact me as soon as possible to talk about your options. Reasonable accommodations will be provided for a brief absence. With proper documentation, extended accommodations will be provided.