Computer Science and Information Technology - Computer Science
12 courses · Palomar College · 2026-2027 Catalog
Classes: Fall 2026 · Spring 2027
CSCI 112 Programming Fundamentals I
- Units
- 4
- Hours
- 3.50 hours lecture, 1.50 hours lab
- Transfers to
- CSU, UC
- C-ID
- COMP 122
Introduction to the basic concepts of Computer Science, the fundamental techniques for problem solving, and the software development process. Includes the syntax and semantics of the C programming language focusing on basic control structures, data types, and input/output.
CSCI 114 Programming Fundamentals II
- Units
- 4
- Hours
- 3.50 hours lecture, 1.50 hours lab
- Prerequisite
- CSCI 112
- Transfers to
- CSU, UC
Object-oriented programming in Java, focusing on classes, instances, methods, interfaces, encapsulation, overloading, file I/O, inheritance, polymorphism, and exception handling.
CSCI 130 Linux Fundamentals
- Units
- 3
- Hours
- 2.00 hours lecture, 3.00 hours lab
- Transfers to
- CSU
An introduction to fundamental end-user skills and tools in Linux, designed for students with little or no command-line Linux or UNIX experience. Emphasis on command line to run shell tools, and to automate routine functions.
CSCI 160 Overview of the Video Game Industry
- Units
- 4
- Hours
- 4.00 hours lecture
- Transfers to
- CSU
Survey of the historical, technological, business, social and psychological aspects of the video game industry. Intended for those considering a career in the video game industry, or those with a strong interest in video games and how they are made.
CSCI 161 Video Game Design
- Units
- 4
- Hours
- 4.00 hours lecture
- Transfers to
- CSU
An introduction to video game design, including the study of various genres of games, and the preparation of a game design document. Intended for those considering a career in the video game industry, or those with a strong interest in video games and how they are made.
CSCI 210 Data Structures
- Units
- 4
- Hours
- 3.50 hours lecture, 1.50 hours lab
- Prerequisite
- CSCI 114
- Transfers to
- CSU, UC
- C-ID
- COMP 132
A systematic study of data structures, including arrays, stacks, recursion, queues, linear and non-linear linked lists, binary trees, hashing, comparative study of searching and sorting algorithms, graphs, Huffman codes, introductory analysis of algorithms, introduction to the complexity of algorithms including big-Oh notation.
CSCI 212 Machine Organization and Assembly Language
- Units
- 4
- Hours
- 3.50 hours lecture, 1.50 hours lab
- Prerequisite
- CSCI 114
- Transfers to
- CSU, UC
- C-ID
- COMP 142
An introduction to Assembly Language programming. Language syntax is covered, together with a study of the instruction set mnemonics, segment, index, pointer, general purpose and flag registers. A variety of memory addressing techniques will be covered, as well as stack operations, particularly those associated with passing parameters to subroutine calls. Also includes I/O to screen, printer, and disk interfaces. Emphasis will be placed on interaction between the student's code and the operating system's supplied functions for I/O to peripheral devices. Use of editor and debugging tools will also be addressed. Students will also be introduced to writing scripting language on a Linux operating system to navigate and compile their programs.
CSCI 222 C++ and Object-Oriented Programming
- Units
- 4
- Hours
- 3.50 hours lecture, 1.50 hours lab
- Prerequisite
- CSCI 114
- Transfers to
- CSU, UC
Detailed study of the C++ programming language and its support for data abstraction and object-oriented programming. Presents an introduction to the fundamental elements of object-oriented programming including encapsulation, classes, inheritance, polymorphism, templates, and exceptions. Students will learn concepts in development of C++ software to scale according to system and hardware demands including algorithm design optimization, efficient memory handling, and efficient coding techniques.
CSCI 250 Introduction to Artificial Intelligence
- Units
- 4
- Hours
- 3.50 hours lecture, 1.50 hours lab
- Prerequisite
- CSCI 114
- Transfers to
- CSU, UC
A hands-on introduction to Artificial Intelligence. Topics include: AI history & applications, neural networks, supervised & unsupervised learning, machine learning programming frameworks, computer vision, text and sequences, AI enabling technologies and limits. The course includes hands-on exercises and projects, in which students will learn how to apply common Artificial Intelligence and Machine Learning algorithms to real world problems in fields of computer vision and natural language understanding.
CSCI 260 Video Game Programming I
- Units
- 3
- Hours
- 2.00 hours lecture, 3.00 hours lab
- Prerequisite
- CSCI 222
- Transfers to
- CSU
Introduction to the programming of video games. Course will explore 3D game development with the current version of DirectX. Students learn how to create 3D games as well as the basics of designing and using a 3D engine. Includes hands-on laboratory experience reinforcing the lecture, text, and course materials.
CSCI 290 Introduction to Machine Learning
- Units
- 4
- Hours
- 3.50 hours lecture, 1.50 hours lab
- Prerequisite
- CSCI 114
- Transfers to
- CSU, UC
A hands-on introduction to Machine Learning. Topics include: Supervised learning, including classification, and regression techniques; Unsupervised learning, including density estimation, clustering, and dimensionality reduction techniques; Reinforcement learning. The course includes hands-on exercises and projects, in which students will learn how to apply the Machine Learning algorithms to real world problems in fields of computer vision and natural language understanding.
CSCI 293 Applications of AI/ML/DL to Real World Problems
- Units
- 4
- Hours
- 3.50 hours lecture, 1.50 hours lab
- Prerequisite
- CSCI 250 or CSCI 290 Recommended Preparation: CSCI 292
- Transfers to
- CSU, UC
This course introduces students to the application of AI/ML/DL to natural language processing, computer vision, signal processing, biology, and more. Students will gain hands-on experience in designing deep learning algorithms, practical experience in building neural networks, and understanding of cutting-edge topics including large language models and generative AI.