ADAN 7220 Mathematical Methods for Machine Learning I

Name: Mathematical Methods for Machine Learning I
Rating: 4.3 (103 reviews)

Machine learning is the design of algorithms that routinely learn and adapt with use to discover hidden properties, patterns, and trends in complex data. This is a semester course on foundational methods in linear algebra and vector calculus to understand the structure and dimensionality of large and complex datasets.

Course overview

Department: Advancing Studies
School: ADV
Credits: 3
Level: Graduate
Offered: Periodically

Requirements fulfilled

No source-backed degree requirement is attached to this course yet.

Official evaluation summary

4.30 / 5

Based on 103 aggregate responses from BC Avalanche/Blue evaluations.

Data freshness

Course and evaluation data last updated 2026-08-02. Source details and limitations are documented in Data Sources and Methodology.

Instructors

Arvind Sharma
Fall 2025, Spring 2025, Fall 2024, Spring 2024 · Official rating 4.60/5
Leslie Servi
Spring 2026 · Official rating 4.00/5
Lorin Gerraughty
Spring 2026, Summer 2025, Midterm Fall 2025, Summer 2024, Spring 2024, Summer 2023, Spring 2023, Midterm Fall 2023, Fall 2022, 2024FALL1 · Official rating 4.61/5
Paul Garvey
Fall 2023 · Official rating 4.30/5

Sections

No current section details are available.