Artificial Intelligence Certificate of Achievement

Program Description

The Artificial Intelligence Certificate is designed to provide students with a comprehensive understanding of AI and its applications in various fields, such as natural language processing, computer vision, robotics, and data analysis. The certificate program is aimed at students who want to develop the knowledge and skills necessary to design and implement AI solutions and to evaluate the ethical and social implications of AI.

The AI Certificate program is composed of a set of courses that cover the fundamentals of AI, including machine learning, deep learning, and neural networks. The courses also provide hands-on experience with AI tools and technologies and give students the opportunity to develop AI solutions for real-world problems. The program also includes courses on the ethical and social implications of AI, which prepare students to evaluate the impact of AI on society and develop AI solutions that are both effective and ethical.

The Artificial Intelligence Certificate is ideal for students who are interested in pursuing careers in a rapidly growing field that is in high demand by employers. The certificate program provides students with the knowledge and skills necessary to succeed in various industries, such as healthcare, finance, and transportation. The program is also suitable for students who want to pursue further education in AI, such as a bachelor's or master's degree in computer science or data science.

The pathway below represents an efficient and effective course taking sequence for this program.  Individual circumstances might require some changes to this pathway.  It is always recommended that you meet with an academic counselor to develop a personalized educational plan.

Division

Academic Services, Business, Computer Technology, and Hospitality

Department

Computer Science

Career and Academic Pathway (CAP)

Business, Information Technology, Hospitality

Program Learning Outcomes

  1. Evaluate and recommend modifications to AI solutions, to ensure they address potential biases and societal impacts effectively.

  2. Demonstrate an understanding of the fundamentals of AI, including machine learning, deep learning, and neural networks.

  3. Apply AI tools and technologies to develop solutions for real-world problems.

  4. Use programming languages and software tools relevant to AI, such as Python and TensorFlow.

  5. Use data analysis and statistical modeling techniques to solve AI problems.

  6. Develop machine learning models for classification and regression problems.

  7. Use cloud computing services and platforms, such as Amazon Web Services (AWS) and Microsoft Azure, to build and deploy AI applications.

  8. Communicate effectively about AI concepts and techniques to technical and non-technical audiences.

Program Map

GE General Education
O Available Online
Program Requirement
P Program Prerequisites

Term 1

10 units
CIT 100 or CS 110
A survey course for the use of software tools such as word processing, spreadsheets, graphics, presentation and database using Microsoft Office.

Course Details:

OR
This course is an overview of the computer science discipline investigating the design and use of the computer devices, the art and science of problem solving and programming, the representation of data, human-computer interactions and ethical considerations, and information security principles. Also included is hands-on experience with command line and GUI operating systems; application of HTML, CSS, and scripts to web pages; and computer programming with an object-oriented language such as C++, Java, or C#.

Course Details:

  1. Transfers to CSU
    2. CIT 103 - Amazon Web Services (AWS) Academy: Cloud Foundations

    The Amazon Web Services (AWS) Academy Cloud Foundations course is intended for students who seek an overall understanding of cloud computing concepts, independent of specific technical roles. It provides a detailed overview of cloud concepts, AWS core services, security, architecture, pricing, and support.

    Course Details:

    CS 102 or CS 102

    This course will cover the most common Python libraries as well as teach you programming best practices. We will explore different aspects of Python, including web, utility applications, machine learning, computer vision, IoT (Internet of Things), and data modeling applications. By the completion of the course, learners will complete a project using Python related to their major or area of interest.

    Course Details:

    1. Departmental Recommendation: MATH 102
      2. or
    2. Departmental Recommendation: MATH 115
    3. Departmental Recommendation: STAT C1000
    4. Departmental Recommendation: STAT C1000H
      5. or
    OR

    This course will cover the most common Python libraries as well as teach you programming best practices. We will explore different aspects of Python, including web, utility applications, machine learning, computer vision, IoT (Internet of Things), and data modeling applications. By the completion of the course, learners will complete a project using Python related to their major or area of interest. This course is intended for students in the Honors Program, but is open to all students who desire more challenging course work.

    Course Details:

    1. Departmental Recommendation: MATH 102
    2. Departmental Recommendation: MATH 115
    3. Departmental Recommendation: STAT C1000
    4. Departmental Recommendation: STAT C1000H

    Term 2

    10 units
    CS 188 - Introduction to Artificial Intelligence (AI)
    This course introduces students to the fundamental concepts and applications of AI, including the various stages of AI project cycles. Moreover, the course will focus on the challenges and issues surrounding AI, such as ethics, bias, culture, regulations, and professional expectations. Students will have a solid understanding of the basics of AI and will be able to apply it to real-world scenarios.

    Course Details:

    1. Prerequisite: CS 110
    2. Prerequisite: CIT 100
    CS 189 - Introduction to Machine Learning
    This course will introduce you to the fundamental concepts and applications of Machine Learning, including data acquisition. Moreover, the course will focus on supervised and unsupervised learning and data modeling. By the end of this course, you will have a solid understanding of the basics of Machine Learning and will be able to apply it to real-world scenarios.

    Course Details:

    1. Prerequisite: CS 110
    2. Prerequisite: CIT 100
    One course from the following:
    This course introduces the essential elements of data science using Python. Although some Python programming knowledge is helpful, it is not a requirement for this general introduction. This course covers data collection, importing, cleansing, wrangling, management, modeling, testing, validating, visualization and presentation. Basic statistical and machine learning techniques are introduced for prediction as well as solving engineering problems. Python programming language and Jupyter notebooks are used for practices.

    Course Details:

    1. Prerequisite: CIT 103

    This course takes students further in Python knowledge and also explores the Python packages and libraries for different applications such as machine learning, data science, and artificial intelligence.  It also utilizes the popular packages such as Numpy, Pandas, MatPlotLib, SKLearn, and Pytorch.  It takes the students through the usage of popular coding notebook with the IPython kernel such as Jupyter, and the popular cloud GPU services.  By the completion of this course, students will have experience using notebook for coding using the aforementioned packages and services to further into area of interest related to programming, especially in the area of data science, machine learning, and artificial intelligence.

    Course Details:

    1. Departmental Recommendation: CS 102
    Introductory course on data collection and management, data manipulation, data modeling, statistical inference, and statistical modeling with data. Students will gain experience using a computer programming language (e.g. Python, R, etc.) to carry out statistical analysis.

    Course Details: