Welcome to an exciting journey into the world of Machine Learning (ML)! ML for Business Applications is a system-driven, project-first ML course that prepares students to design, justify, and reason about real-world AI/ML systems in enterprise settings. The course retains the intellectual foundations of traditional ML while reorienting them toward modern, deployable, business-aligned ML architectures.

Rather than surveying ML as a collection of disconnected algorithms, this course treats ML as an end-to-end system—from problem framing and agent definition (via PEAS) through data pipelines and model selection to evaluation, deployment considerations, cost–quality trade-offs, and ethical governance. Students learn how AI systems are actually built, evaluated, and approved within organizations, not just how models work in isolation.

The course follows a carefully sequenced structure optimized for successful project delivery. Early weeks establish a shared understanding of intelligent agents and system goals. Mid-semester content focuses on pipelines, representations, learning paradigms, and evaluation metrics to prevent downstream design failures. Only after students have sufficient technical and conceptual maturity do they engage with modern enterprise ML capabilities such as embeddings, transformers, retrieval-augmented generation (RAG), cloud-based architectures, and cost/ROI analysis.

This course deliberately de-emphasizes theory-first topics such as symbolic logic, classical search, formal planning, and probabilistic graphical models in favor of competencies that define today’s enterprise ML landscape:

• agent-oriented system design

• data and ML pipelines

• model selection and evaluation (not algorithm invention)

• LLMs, embeddings, and semantic retrieval

• cloud deployment reasoning

• operational cost, risk, and governance

• ethical and organizational implications of ML systems

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Students work in teams on a semester-long project where they design an ML system, prototype components using university infrastructure, and reason rigorously about deployment choices, scalability, cost, and business value. Assessment emphasizes system-level reasoning, design justification, and business alignment, rather than exam-centric memorization or algorithmic breadth.

This course reflects the post-LLM reality of enterprise ML and is intentionally differentiated from canonical, theory-first ML curricula. It prepares students to operate effectively at the intersection of AI/ML technology, business strategy, and responsible system design.

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Prerequisites: Working knowledge of Python is required.

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Technical Requirements: Students are required to have access to a fairly recent laptop or desktop computer with a current operating system.

• A reliable high-speed Internet connection and a modern web browser (Chrome or Firefox recommended) are required.

• This course relies on both in-person and remote collaboration. Students must install and use the Zoom client on a computer or tablet to participate in office hours and out-of-class meetings, including group project discussions.

• Course labs, homework assignments, and the semester project will utilize the SDSU Instructional Cluster to access server-based compute and AI acceleration (GPU resources).

• Students will work primarily in JupyterLab, developing solutions in Python and PyTorch within a managed computing environment designed to support modern ML workflows.

• Access instructions and usage documentation for the SDSU Instructional Cluster are available at:
  https://sdsu-research-ci.github.io/instructionalcluster

TA's & Office Hours

Meeting Times: Tuesdays & Thursdays 5:30 PM - 6:45 PM

Class Location: GMCS 324

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Teaching Assistants: 

1. Vinay Hiroo Surtani (vsurtani8879@sdsu.edu )

Thursdays, 1:00 PM - 2:00 PM & Thursdays, 5:30 PM - 6:45 PM (during the class session) 

in-person: GMCS 549; and over Zoom https://sdsu.zoom.us/j/5906628633

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2. Caleb Dickson ( cdickson9858@sdsu.edu )Tuesdays, 4:30 PM - 5:30 PM

in-person: GMCS 549; and over Zoom https://SDSU.zoom.us/j/81574017345

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3. Devashish Palate (dpalate4185@sdsu.edu )Thursday 1:00 PM - 3:00 PM

in-person: GMCS 549; and over Zoom https://sdsu.zoom.us/j/85817325967

Course Schedule (2026)