EECE 5644 — Introduction to Machine Learning and Pattern Recognition

4 semester hoursGraduateLectureusually offered: fall, springtypical days: T/W/FBostonOnlineSeattle, WATraditionalVideo Streaming

Studies machine learning (the study and design of algorithms that enable computers/machines to learn from experience/data). Covers a range of algorithms, focusing on the underlying models between each approach. Emphasizes the foundations to prepare students for research in machine learning. Topics include Bayes decision theory, maximum likelihood parameter estimation, model selection, mixture density estimation, support vector machines, neural networks, probabilistic graphics models, and ensemble methods (boosting and bagging). Offers students an opportunity to learn where and how to apply machine learning algorithms and why they work.

Prerequisites

Offering history

TermSectionsEnrolledCapacityFullOpen seats/section
Fall 202348821741%32.3
Spring 202435619928%47.7
Summer A 20241314078%9.0
Fall 20242589859%20.0
Spring 20252476177%7.0
Summer A 20251234947%26.0
Fall 2025413426850%33.5
Spring 2026415018282%8.0

Snapshots from scheduled scrapes — not live seat availability. "Full" can exceed 100% when sections over-enroll.

Meeting times

Share of recent sections by weekday: M 17% · T 57% · W 50% · Th 14% · F 59%

Common patterns: TF (35% of sections), WF (24% of sections), MTWR (9% of sections), W (9% of sections), MW (8% of sections), T (8% of sections) — in patterns, R means Thursday

Professors

Fall

Spring

Summer A

Percentages are each professor's average share of the season's enrolled students in recent terms.

Unlocks

CHME 6580, CS 6170, CS 6180, CS 7140, CS 7150, DS 5500, EECE 5645, EECE 5668, EECE 7345, EECE 7373, EECE 7397, HLTH 5810, MATH 7339

Courses that list EECE 5644 in their prerequisites.

Links

Official catalog (EECE course descriptions) · Student reviews on RateMyHusky · All EECE courses · Plan it at numap.app