Department of Computer Science

DEPARTMENT
OF
COMPUTER
SCIENCE

Where rigorous computing meets ambitious ideas.

Northridge's Department of Computer Science trains students to build systems that hold up under real conditions, and to ask hard questions about what those systems should do in the first place. Undergraduate through doctoral study, across artificial intelligence, security, data systems, software engineering, and human-computer interaction.

1,240

Students

Faculty & Researchers

Active Research Projects

Courses in the Explorer

Specializations

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Signature Feature

Find any course the way you'd actually look for it: by level, then semester, then course.

Every one of the department's 32 courses has its own page — full description, lecturer, credits, prerequisites, and a complete week-by-week breakdown. Start browsing below, or open the full Course Explorer.

100 Level

Foundations year

First Semester (4)Second Semester (4)

200 Level

Core methods year

First Semester (4)Second Semester (4)

300 Level

Specialization year

First Semester (4)Second Semester (4)

400 Level

Capstone year

First Semester (4)Second Semester (4)

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Academic Pathway

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Programs

Undergraduate and postgraduate pathways

All programs

4 years · Full-time, on campus

B.Sc. Computer Science

Broad computing foundation with room to specialize in the final two years.

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4 years · Full-time, on campus

B.Sc. Software Engineering

Engineering-oriented track emphasizing large-scale software design, process, and quality.

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2 years · Full-time or part-time

M.Sc. Computer Science

Advanced computer science with a specialization track chosen in the first semester.

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Featured Courses

A sample from the current catalog

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Core100 Level

CSC101

Introduction to Computing

Introduction to Computing is most students' first formal encounter with computational thinking. The course treats programming as a way of expressing precise ideas, not as a set of syntax to memorize: students learn to break problems into steps, represent information in ways a machine can use, and read their own logic critically before a computer ever runs it. By the end of the semester, students are writing small but complete programs and can explain, in plain language, what every line of their code is for.

Machine Learning covers the core supervised and unsupervised learning methods, how to evaluate them honestly, and where they tend to fail. Every technique is paired with a discussion of its assumptions, so students learn to ask what a model actually knows, not just what it outputs.

Sofia Reyes 4 cr

Core300 Level

CSC301

Software Engineering Principles

Software Engineering Principles scales up everything students have learned about writing individual programs into the practice of building software as a team, over time, under changing requirements. Students work in project groups across the semester, experiencing requirements drift, code review, and the cost of a design decision made too quickly.

Farah Haddad 4 cr

Core300 Level

CSC306

Cybersecurity Fundamentals

Cybersecurity Fundamentals covers the core concepts defenders and attackers both need to understand: threat modeling, common vulnerability classes, and the basic cryptographic tools used to protect systems. Labs run in an isolated environment built specifically for safe, hands-on security practice.

Marcus Webb 3 cr

Research

Active work across seven research groups

All research

EvaluationArtificial Intelligence & Machine Learning

Explainable Decision Support for Clinical Triage

This project develops triage-support models that surface the specific evidence behind a recommendation, so clinical staff can agree, override, or ask for more information rather than simply trusting a score. The team is now running structured evaluations with partner clinicians to test whether the explanations actually change decisions for the better.

Intelligent Systems Lab Elena Marchetti

PrototypeCybersecurity

Adaptive Intrusion Detection for IoT Networks

Home and campus IoT devices are cheap, numerous, and rarely patched. This project builds a lightweight detection layer that learns a device's normal behavior and flags deviations without needing constant vendor updates. A working prototype is deployed on a segment of the campus residence network under controlled conditions.

Secure Systems Group Marcus Webb

PublicationData & Knowledge Systems

Federated Learning for Privacy-Preserving Analytics

Hospitals and clinics often can't share patient data directly, even for research that could help their own patients. This project's federated learning framework trains shared models across institutions without the raw data ever leaving its source. Two partner hospital research networks are using an early version in production.

Data & Knowledge Systems Lab Priya Raghunathan

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