Advanced Topics in Machine Learning

UVa CS 8501 (Fall 2022)

Rubrics and Grading Policy

1. Question assignments

1.1 Grading Rubric

Points Requirements
0.5 Related to the lecture topic (base score).
+ 1.0 Clear definition of the question.
+ 1.0 Provide necessary references to the textbook or other complementary readings.
+ 1.0 Explain what the challenge was when you tried to answer this question.

Comments

2. Discussion assignments

2.1 Grading Rubric

Points Requirements
1.0 Correctly identifies the question. Answer is directly related to the topic of the question (base score).
+ 1.0 Answers the question in its entirety. If there are related subcomponents to the question, answer addresses each subcomponent.
+ 1.0 Sufficient justification about why you think this is an answer to the question.
+ 1.0 Necessary references for readers to 1) further understand the technical details and/or 2) know which section in the textbook they can refer back to.

Comments

3. Final project

3.1 Grading Rubric for Final Project Proposal

A final project proposal should cover the following components

Additional specification

3.2 Grading Rubric for Final Report

4. Class attendance

Class attendance is an important component for this course. Therefore, we plan to take class attendance in every class time. Missing class without permission will cause some point deduction.

Missing classes Point deduction
1 < n <= 2 -1.0
2 < n <= 4 -2.0
4 < n <= 6 -3.0
6 < n <= 8 -4.0
n > 8 -5.0

5. Grade Mapping

At the end of this semester, students can calculate their grades based on the following point-to-grade mapping.

Point range Letter grade
[99 100] A+
[94 99) A
[90 94) A-
[88 90) B+
[83 88) B
[80 83) B-
[74 80) C+
[67 74) C
[60 67) C-
[0 60) F

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