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Virginia Tech

ECE-4424: Machine Learning

Description: Algorithms and principles involved in machine learning; focus on perception problems arising in computer vision, natural language processing and robotics; fundamentals of representing uncertainty, learning from data, supervised learning, ensemble methods, unsupervised learning, structured models, learning theory and reinforcement learning; design and analysis of machine perception systems; design and implementation of a technical project applied to real-world datasets (images, text, robotics). A grade of C- or better in prerequisites.

Pathways: N/A

Course Hours: 3 credits

Prerequisites: (CS-2114 or ECE-3514) and (CMDA-2006 or STAT-3704 or STAT-4105 or STAT-4604 or STAT-4714 or STAT-4705)

Required By: ECE-4805, ENGE-4735

Corequisites: N/A

Crosslist: CS-4824

Repeatability: N/A

Sections Taught: 23

Average GPA: 3.00 (rounds to B)

Strict A Rate (No A-) : 23.19%

Average Withdrawal Rate: 7.87%

Dawei Zhou202342.9%35.7%7.1%0.0%0.0%14.3%3.341
Hoda M Eldardiry202435.7%35.7%14.2%0.0%0.0%14.3%3.251
Chris L Wyatt20226.4%28.6%22.4%9.4%5.6%27.6%2.234
Amr E Hilal202312.1%47.3%15.6%16.0%5.0%4.0%2.434
Debarati Bhattacharya202446.6%29.1%11.3%5.2%1.3%6.5%3.222
Bert Huang202360.7%27.6%7.5%2.4%0.0%1.8%3.453
Hongjie Chen2022100.0%0.0%0.0%0.0%0.0%0.0%3.911
Ming Jin202447.1%41.2%8.7%0.8%0.7%1.5%3.335
Debswapna Bhattacharya202468.4%21.1%5.3%0.0%5.3%0.0%3.471
Anuj Karpatne202031.6%42.1%21.1%0.0%5.3%0.0%3.011

Grade Distribution Over Time

1234GPA
Spring 2019Fall 2019Spring 2020Fall 2020Spring 2021Fall 2021Spring 2022Summer I 2022Fall 2022Spring 2023Fall 2023Spring 2024Fall 2024Term050% W