Program Information
The Bachelor of Science in Applied Machine Learning prepares students for careers in fields that require application and development of machine learning algorithms in order to automate processes, create models, and gain better insights in a variety of contexts. Students will learn a combination of statistics and computer science concepts with practical applications in machine learning, such as: programming, algorithms, natural language processing, and deep learning. The curriculum highlights real-world applications so that students develop strong communication, problem-solving, and critical thinking skills. Students who successfully complete this program will be able to find solutions not only based on academic theory, but also by weighing factors that are relevant to the situation. This combination will make them an asset for organizations in a variety of industries, including finance, commerce, healthcare, technology, and other areas where reliable predictions and systematic decision making provide an advantage.
Student Learning Outcomes
1. Understand the mathematical foundations of machine learning: calculus, statistics, and linear algebra.
2. Demonstrate proficiency in the computer science foundations of machine learning: data structures and Python.
3. Identify and distinguish machine learning methods and apply them to appropriate problems and circumstances.
4. Use resources in available libraries (e.g. Pandas, Scikit, TensorFlow, PyTorch) to create machine learning models.
5. Apply supervised and unsupervised learning for data analysis.
6. Classify data using logistic regression.
7. Apply regression analysis to predict outcomes.
8. Understand Natural Language Processing, apply NLP, and identify potential improvements and uses.
9. Construct and train a neural network for optimal performance (convolutional and recurrent neural networks)
10. Evaluate models selecting appropriate data sets and metrics.
11. Translate problems from ordinary language to computer languages.
12. Comprehend results produced by algorithms and apply those to addressing organizational problems.
13. Identify, explain, and analyze ethical problems in the interactions of technology and society (e.g. bias, privacy, freedom).
Requirements for the Bachelor of Science in Applied Machine Learning
The degree requires completion of 120 units as follows: 36 units of general education, 45 units for the major, and 39 units of elective courses, including courses taken to earn minors. (See Declaring Minors below for more information.) Each course listed carries three semester units of credit, unless otherwise noted. A cumulative grade-point average of 2.00 “C” or higher is required in all courses taken at Golden Gate University.
All degree-seeking undergraduate students must complete their English, mathematics and critical thinking requirements within their first 27 units at Golden Gate University, unless they have already earned credit for the equivalent courses from another institution and have had those courses accepted in transfer by Golden Gate University. If either math or English requirements for the degree have not been satisfied, newly enrolled students must take placement tests to ensure proper placement in the appropriate math or English course. Students may also choose to waive the placement tests and enroll in the first course in either series, which are ENGL 10A and MATH 10 . (See the course descriptions below to identify courses that have prerequisite course requirements.)
Undergraduate Honors Program
The School of Undergraduate Studies’ honors program provides opportunities for students enrolled in all degree programs to engage in enriched learning experiences while they work toward earning their degrees. Students do not need to apply separately for this program, but may participate in it by registering for honors-designated course sections, as described below. Upon graduation, students who have completed the honors program must complete and submit the Honors Program Notation Request form to the Registrar’s Office to have the notation added to their transcripts.
Honors-designated course sections will emphasize the following learning outcomes: media and information literacy, quantitative fluency, oral/written communication, and critical thinking. Students will be required to complete advanced and more rigorous assignments that demonstrate learning beyond the articulated course outcomes. Additional assessments will be designed to emphasize core skills such as critical thinking, writing, research, and self-reflection.
Program Requirements
To complete the honors program, students must complete any combination of 12 units (four 3-unit courses) of honors-designated sections and an honors-designated capstone course section (3 units) for a total of 15 units, with a minimum GPA in the five honors courses of 3.00 and a minimum overall degree program GPA of 3.30. Note: honors course sections can be identified in the online course schedule with a section prefix beginning with the letter “A” (e.g., ASF1) and by information in the section comments field of the section details page. Students should contact their academic advisor or the Registrar’s Office if they need help identifying honors-designated course sections.
Honors sections of the courses below will be offered every term. In addition, students who transfer any of these courses into GGU may petition to have an honors section offered of other courses in order to satisfy the 12-unit requirement. Students should contact their academic advisors to begin the petition process.
Core Requirement - 12 units
Capstone Course - 3 units
Complete the honors section of the capstone course applicable to the student’s degree program.
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