Course Staff

Logistics

• Office hours and drop-in peer mentoring: See the calendar below or here. The instructor and TA’s will have regular office hours every week. Course producers will have drop-in peer mentoring sessions before each assignment is due.

• Assignments: Assignments should be submitted through Gradescope. Feedback will also be provided on Gradescope. Please enroll in with entry code: 6JK3ZJ.
  You should submit both your PDF writeup and your code on Gradescope; there will be separate assignments for each.
• Discussions: We will be using Piazza for general course-related questions and announcements. All enrolled students were added at the beginning of the semester; you can also use this sign-up link. If you have an individual matter to discuss, email me directly (please put “CSCI 467” in the subject line) or come to my office hours. For grading questions, go to the office hours of the person who graded the problem in question.

Prerequisites

• Algorithms: CSCI 270
• Linear Algebra: MATH 225
• Probability: EE 364 or MATH 407 or BUAD 310

This class will also use some basic multivariate calculus (taking partial derivatives and gradients). However, knowledge of single-variable calculus is sufficient as we will introduce the required material during class and section.

All programming assignments will be in Python. Basics of Python will be covered in discussion sections. Students who are not familiar with Python may need to spend some time becoming more familiar with it as needed.

Schedule

All assignments are due by 11:59pm on the indicated date.

Slides and lecture materials will be uploaded after each class.

Grading

Grades will be based on homework assignments (40%), a class project (20%), and two exams (40%).

Homework Assignments (40% total):

• Homework 0: 4%
• Homeworks 1-4: 9% each

Final Project (20% total). The final project will proceed in three stages:

• Project proposal: 2%
• Midterm report: 3%
• Final project report: 15%

Exams (40% total):

• In-class midterm: 15%
• Final exam: 25%

Late days

You have 6 late days you may use on any assignment excluding the Project Final Report. Each late day allows you to submit the assignment 24 hours later than the original deadline. You may use a maximum of 3 late days per assignment. If you are working in a group for the project, submitting the project proposal or midterm report one day late means that each member of the group spends a late day. We do not allow use of late days for the final project report because we must grade the projects in time to submit final course grades.

If you have used up all your late days and submit an assignment late, you will lose 10% of your grade on that assignment for each day late. We will not accept any assignments more than 3 days late.

Late submissions not covered by these 6 late days will incur a 10% grade deduction per day.

Final project

The final project can be done individually or in groups of up to 3. This is your chance to freely explore machine learning methods and how they can be applied to a task of our choice. You will also learn about best practices for developing machine learning methods—inspecting your data, establishing baselines, and analyzing your errors. More information about the final project is available here.

Resources

Prof. Robin Jia have written Lecture Notes that accompany all the iPad lectures. I recommend using these notes as reference material for studying. There is no required textbook for this class. Note that, we have slight modifications of the materials to accormadate to the fall semester.

If you do want to learn from a textbook, the following may be useful:

• Probabilistic Machine Learning: An Introduction (PML) and Probabilistic Machine Learning: Advanced Topics (PML2) by Kevin Murphy. You may also find PML Chapters 2-3 and 7 useful for reviewing prerequisites.
• The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
• Patterns, Predictions, and Actions: A Story about Machine Learning by Moritz Hardt and Benjamin Recht
• Fairness and Machine Learning: Limitations and Opportunities (FAML) by Solon Barocas, Moritz Hardt, and Arvind Narayanan.

To review mathematical background material, you may also find the following useful:

• Linear Algebra: You don’t need to remember many advanced theorems, but you need to be very comfortable with the basics (dot products, Euclidean distance, matrix multiplication, matrix invertibility, etc.). To review these concepts, I recommend using 3blue1brown’s linear algebra videos. You can skip chapters 6, 10-12, and 16.
  • If you also want a textbook, my recommendation is Introduction to Applied Linear Algebra by Stephen Boyd and Lieven Vandenberghe. Most relevant reading: Chapters 1-3, 5-8, 10-11. (Chapters 4 and 12-14 overlap with content for this class.)
• Probability: I recommend Introduction to Probability by Joseph Blitzstein and Jessica Hwang. Reading Guide:
  • Chapter 1: Optional but good background, recommended to read briefly.
  • Chapter 2: Important for class, read carefully. (2.7-2.8 are optional but good for building understanding)
  • Chapter 3: Read 3.1-3.3, 3.7-3.8.
  • Chapter 4: Read 4.1-4.2, 4.4-4.6.
  • Chapter 5: Read 5.1 and 5.4.
  • Chapter 7: Read 7.1, 7.3, and 7.5. (Will only be relevant after the midterm exam)
• Multivariate Calculus: Oliver Knill’s lecture notes. Recommended reading: Lectures 11, 14, 15, 16, 17.

Other Notes

Collaboration policy and academic integrity: Our goal is to maintain an optimal learning environment. You may discuss the homework problems at a high level with other students, but you should not look at another student’s solutions. Trying to find solutions online or from any other sources for any homework or project is prohibited, will result in zero grade and will be reported. Using AI tools to automatically generate solutions to written or programming problems is also prohibited. To prevent any future plagiarism, uploading any material from the course (your solutions, quizzes etc.) on the internet is prohibited, and any violations will also be reported. Please be considerate, and help us help everyone get the best out of this course.

Please remember the expectations set forth in the USC Student Handbook. General principles of academic honesty include the concept of respect for the intellectual property of others, the expectation that individual work will be submitted unless otherwise allowed by an instructor, and the obligations both to protect one’s own academic work from misuse by others as well as to avoid using another’s work as one’s own. All students are expected to understand and abide by these principles. Suspicion of academic dishonesty may lead to a referral to the Office of Academic Integrity for further review.

Students with disabilities: Any student requesting academic accommodations based on a disability is required to register with Disability Services and Programs (DSP) each semester. A letter of verification for approved accommodations can be obtained from DSP. Please be sure the letter is delivered to the instructor as early in the semester as possible.