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This course is meant to introduce students to machine learning and deep learning. ME 107 is taught at the undergraduate level and teaches students how to identify a machine learning problem in the context of real-world applications, mathematically formulate a learning problem, identify when a learning problem is well-posed, under-determined and overdetermined, and develop algorithms and Python code to solve the problem. Students are introduced to the concepts of learning algorithms, overfitting, under-fitting, statistical measures of estimators, optimization approaches to learning, principal component analysis, regression, support vector machines, and artificial neural networks.

Prerequisites: ME 17; or consent of instructor

3

Units

Letter

Grading

1, 2, 3

Passtime

Upper division only

Level Limit

Engineering

College
ZIMMAN L
Lal Zimman
4.6
13 reviews
YEUNG E H
Enoch Yeung
4.6
11 reviews
AI predicted, based on past grading trends of the course and instructor, class info, and 127 other factors
Fall 2025 . Yeung E H
BUCHN1910
T R
15:30 PM - 16:45 PM
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ME 107 Yeung E H Fall 2025 Total: 96
ME 107 Yeung E H Fall 2024 Total: 92
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24
4.6
ME107 . Yeung E H 3 Months Ago

Professor Yeung is extremely focused on being kind and adaptive. He made an honest attempt to learn the names of all ~100 of us. Even with all of this nicety, his ability to blend high and low level concepts approachably also allowed for effective learning. Exams are easy, and homework is only challenging before he essentially hands out solutions.

0 helpful 0 unhelpful
ME155A . Yeung E H 2 Years Ago

Great professor all-round. Understanding, answers questions with responses tailored to EVERYONE. Posts notes online pretty much day-of, gives gracious extensions and extra credit to patch up your grade, but still expects you to try your best. Dulls the edge of new and difficult material and makes it enjoyable. Archetype of modern lecturers.

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LING132 . Zimman L 3 Years Ago

This class was essentially cut in half due to the TA strike, so my grade was only based on 1 paper, the midterm, & participation. Attendance wasn't mandatory but class lectures were interesting. All the papers were super straightforward and short. The midterm was weirdly hard and lacked structure but they were flexible about dropping questions.

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ME125EY . Yeung E H 4 Years Ago

he box jumped onto the table. incredible athlete. respecc fosho fosho

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LING130 . Zimman L 4 Years Ago

Professor zimman was incredibly laid back. Lecture was fairly interesting and data analysis papers were really pretty simple if you go to lecture. Jamal was an amazing TA and clarifies exactly how to do well on the data analyses. Final was fairly tough though. Attendance does not matter which is a plus. Records all lectures and understands GS.

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ME155A . Yeung E H 4 Years Ago

Prof Yeung is an incredibly smart professor, sometimes to a fault because I don't think he fully realizes how hard this material (controls theory) is to many students. But he is really caring and accommodating with homework extensions and shorter or simpler homework. Always willing to answer questions and helpful office hours. Hw, midterm, final.

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