Welcome to CSE/STAT 416 Introduction to Machine Learning!
Syllabus
- Instructor: Valentina Staneva (vms16@uw.edu)
- Office Hours: 11:30am - 1:30pm
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Lectures: Tuesdays & Thursdays, 10:00-11:20am, Online via Zoom
- Quizz Sections: Thursdays
- AA: 12:30 - 1:20pm (Anne Wagner/Hongjun Jack Wu)
- AB: 1:30 - 2:20pm (Anne Wagner/Hongjun Jack Wu)
- AC: 2:30 - 3:20pm (Gang Cheng/Seth Temple)
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AD: 3:30 - 4:20pm (Gang Cheng/Seth Temple)
Check office-hours for more information.
Description
Provides practical introduction to machine learning. Modules include regression, classification, clustering, dimensionality reduction with a focus on an intuitive understanding grounded in real-world applications. Intelligent applications are designed and used to make predictions on large, complex datasets.
- CSE 143 or CSE 160 (programming experience); and STAT 311 or STAT 390 (prob & stats experience).
If you are missing some of the prerequisite material, you are responsible to learn it on your own (check out the resources page).
Learning objectives:
- Convert real world problems to machine learning problems
- Distinguish between different types of machine learning problems
- Preprocess unstructured datasets for machine learning
- Know and apply multiple machine learning methods and recognize their assumptions
- Employ contemporary machine learning libraries to solve practical problems
- Evaluate the performance of machine learning algorithms**
Assessment
Here is how your grade will be calculated:
- Homeworks: 80%
- Programming Portion: 60%
- Concept Portion: 20%
- Final Exam: 20%
This maps to the 4.0 grade scale roughly as follows. You will get at least the grade below for the percentages shown:
- 90% at least 3.5
- 85% at least 3.0
- 80% at least 2.5
- 75% at least 2.0
- 70% at least 1.5
- 60% at least 0.7
Homework
Most weeks, there will be a homework assignment that is generally divided into two parts. The first is a programming assignment that will involve writing Python code, processing and analyzing data, and answering open ended questions all based on that analysis. The second part focuses on answering conceptual questions at a high level or might involve doing some calculation by hand. Each of these portions contribute to your grade separately as per the section above.
Unless otherwise stated, homework assignments should be completed individually by each student without the use of materials outside the class. Please refer to the Academic misconduct policy.
Each student receives 2 “late days” for use on homework assignments. A late day allows you to submit a program up to 24 hours late without penalty. For example, you could use 2 late days and submit a program due Thursday 11:30pm on Saturday by 11:30pm with no penalty. Once a student has used up all the late days, each successive day that an assignment is late will result in a loss of 10% on that assignment. Regardless of how many late days you have, you MAY NOT submit a program more than TWO days after it is due. Leftover late days have no impact on your grade.
Further, one is allowed to drop one homework score: keep this for emergencies.
If unusual circumstances truly beyond your control prevent you from submitting assignments or attending an exam on time, you should discuss this with the course staff ASAP. If you contact us well in advance of the deadline, we may be able to show more flexibility in some cases.
Exam
Correction: The exam is now optional. It will be still available between 10:30am PDT to 10:30pm PDT on Gradescope and you will have 5 hours to complete the exam once you open it. The extra two hours are for your convenience. There will not be more questions due to that. Make sure you save your work.
The exam will be available on Gradescope from 10:30am PDT to 10:30pm PDT and you will have 3 hours to complete the exam once you open it. This time should be enough if you are familiar with the material and can allow for some small unexpected interruptions. If you have extra time left: double check your answers. Here is the practice exam from last year practice exam and the solutions. Here is also a checklist of topics that can help you review topics checklist.
Academic honesty and collaboration
We take academic honesty very seriously. Cheating not only deprives you of practice and understanding, but is also unfair to the rest of your class. Therefore, please restrict attention to the materials provided as a part of this course (lecture slides, quiz section handouts, etc.) when solving the programming assignments and concept quizzes and do not copy answers from anywhere. If you do happen to use other material, it must be acknowledged clearly with a citation on the submitted solution. Reading unauthorized material will be considered cheating. Please ask us if you unsure about the use of a particular reference.
You are still, however, encouraged to discuss assignments and course content on a high level with your classmates so long as you:
- Make a serious attempt on each and every problem or coding project before discussing it with others.
- Limit cooperation to just group discussion and brainstorming. A good rule of thumb is that you should not take away any written notes, photographs, or other records from your brainstorming session.
- Do not show your code to another student, nor should you look at another student’s code directly. This includes current and former students, tutors, friends, paid consultants, people on the internet etc…
- Do not post your homework solution code publicly online for any reason.
- Some of the concept questions might have multiple choice answers. These are generally much more difficult to talk about at a high level since we don’t want you telling anyone which answers are correct or incorrect. Again, your discussions should be at a high level and should not discuss the actual answers.
- Write up each and every problem in your own writing, using your own words. (Similarly, you must write code on your own).
- List each person you collaborated with at the top of your written homework or in your project writeup and which parts you collaborated on.
If you have a specific question about your code or written work, the only person you may show your work is a member of the course staff.
Cheating: If you do not follow these rules, you will be considered to have cheated. Cheating is a very serious offense. If you are caught cheating, you can expect a failing grade and initiation of a cheating case in the University system. Cheating is an insult to other students, to the instructor, and to yourself. If you feel that you are having a problem with the material, or do not have time to finish an assignment, or have any number of other reasons to cheat, we have many mechanisms for getting help. Copying the work of others is not the solution.
For more details, see this Academic Misconduct page. If you’re not sure if something constitutes cheating, send the instructor an email describing the situation.