# CMPUT 466/566: Introduction to Machine Learning

### Winter 2020

### Instructor: Lili Mou

### Additional Materials (requiring UofA sign-in)

Course logistics

Course project

Cheatsheet for Math

Written Assignments

Coding Assignments

Since I release all materials on my homepage, there is no point in being added to eclass.

Manually adding every unofficial auditing student to eclass takes too much time for me.
### Course format

The instructor will derive everything on the whiteboard. No slides would be used.

No official textbook is designated, but the instructor will provide lecture notes and

point to materials for further reading.
### Prerequisites

- Basic math (including algebra, calculus, probability theory, and statistics)
- Coding skills. The students should be able to write code in at least one programming

language, although the programming assignments would be in python.

**No** machine learning background is required, as this could serve as the first course in machine learning.
### Grading Schema

**Undergraduate students:**
- Written assignments: 10%
- Coding assignments: 10%
- Small project: 10% (Do something for something)

+ 5% bonus for a non-trivial project
- Mid-term (closed book): 30%
- Final (closed book): 40%

**Graduate students:**
- Written assignments: 5%
- Coding assignments: 10%
- Course project: 10% for doing something for something

+ 5% for a non-trivial project
- Mid-term (closed book): 30%
- Final (closed book): 40%

Note:
- This course focuses on paper-based derivation instead of programming.
- To avoid any disputation of marking, written assignments will be graded by

a binary score for each problem. 1=a serious attempt, 0=otherwise.
- For coding assignments and exams, a student can appeal for the marks only due to

scientific reasons (e.g., comments are significantly, factually wrong). A student

cannot dispute for partial mark deduction if the solution has an error. (Theoretically,

no partial
marks should be given for wrong answers. If there were any partial marks,

they would
reflect TA's or the instructor's courtesy.)
- The final letter grade will be given by some cut-off based on numerical marks. Assuming

a student does reasonably well in all assignments and the course project, then the letter

grades roughly maps to the following criteria:
- A+ = The student well understands lecture materials and can generalize to new problems.
- A = The student well understands lecture materials but is unable to generalize to new problems.
- A- = The student understands most part of the lecture materials, but a few details are missing.
- B+ = The student understands some part of the lecture materials, but a significant portion are missing.
- B = The student has some qualitative/philosophical understanding of lecture materials, but does only a little quantitative derivation.