## About the Course and Prerequisites

Machine learning explores the study and construction of algorithms that can learn from historical data and make inferences about future outcomes. This study is a marriage of algorithms, computation, and statistics so this class will be have healthy doses of each. The goals of this course are to provide a thorough grounding in the fundamental methodologies and algorithms of machine learning.

Prerequisites: Students entering the class should be comfortable with programming and should have a pre-existing working knowledge of linear algebra (MATH 308), vector calculus (MATH 324), probability and statistics (MATH 394/STAT390), and algorithms. For a brief refresher I recommend you consult the linear algebra and statistics/probability reference materials below.

## Textbook and reference materials

I will assign reading out of the following texts because they are excellent and their PDFs are offered for free by the authors.
If you buy one ML book, I would recommend HTF of above. If you buy an additional ML book, I would recommend Shalev-Schwartz and Ben-David of below.
You may also find these reference materials useful throughout the quarter.

## Grading and Evaluation

Your grade will be based on 5 homework assignments (65%) and a final project (35%).

### Homework

Your homework score will be the smaller of 100 points and the cumulative number of points you receive on the assignments. The first homework is worth 10 points, and the final four are worth 25 each. This means if you receive grades $(x_0,x_1,x_2,x_3,x_4)$ you will receive a score of $\min(100, x_0+x_1+x_2+x_3+x_4)$. In particular, if you receive grades

• $(10,25,25,25,0)$ you will get a total homework score of $85$.
• $(10,25,25,25,15)$ you will get a total homework score of $100$.
• $(10,25,25,25,25)$ you will get a total homework score of $100$.
Homeworks must be submitted by the posted due date at 11:59 PM Seattle time.
• Late work will receive a score of 0.
• All assignments must be submitted (even if late for a score of 0). If not, you will not pass.
• All assignments are to be submitted electronically on canvas.

Each homework assignment contains both theoretical questions and will have programming components.

• You are required to use Python for the programming portions. There are a number of Python resources above. You may use any numerical linear algebra package (e.g., NumPy/SciPy), but you may not use machine learning libraries (e.g. sklearn, pytorch, tensorflow) unless otherwise specified (later in the course). YOur analysis and code should all be included in a single PDF, with your code at the end very end.
• You must submit your HW as a typed PDF document typeset in Latex (not handwritten). There are a number of Latex resources above. Also note that LaTeX is installed on department-run machines.

The first homework (10 points) is designed to be a review of the course prerequisites. If this assignment requires significant effort (e.g., several hours) or contains unfamiliar topics, you should strongly consider dropping the course and revisiting the prerequisites. Its secondary purpose to get you comfortable with Python and Latex.

COLLABORATION POLICY: Homework must be done individually: each student must submit their own answers. In addition, each student must write and submit their own code in the programming part of the assignment (we may run your code). It is acceptable, however, for students to collaborate in figuring out answers and helping each other solve the problems. You must also indicate on each homework with whom you collaborated.

LATE POLICY: Homeworks must be submitted online by the posted due date. With the exception of the poster presentation, all work is to be submitted online. There is no credit for late work. The homework scoring system of above is an attempt to minimize the rigidness of this policy. We may make special arrangements for alternative dates for poster presentation (contact the instructors). If you are unable to meet the deadlines due to travel, conferences, other deadlines, or any other reason, do not enroll in the class.

### Project

You will work independently or with a partner on a machine learning project spanning most of the quarter ending with a poster presentation and written report. You may use techniques developed in this course but are also encouraged to learn and apply new methods. The project should address a novel question with a non-obvious answer and must have a real-data component. We will provide some seed project ideas. You can pick one of these ideas, and explore the data and algorithms within and beyond what we suggest. You can also use your own data/ideas, but, in this case, you have to make sure you have the data available at the time of the proposal and a nice roadmap, since a quarter is too short to explore a brand new concept. The components of the project are

• Project Proposal (10 points): A one page maximum description of your project with: 1) project title, 2) dataset(s), 3) Project idea (two paragraphs), 4) Software you will write and/or use, 5) papers to read (include 1-3 relevant papers), 6) will you have a teammate?, and 7) what will you complete by the milestone (experimental results are expected)?
• Project Milestone (15 points): Your write up should be 3 pages maximum (not including references) in Camera-ready NIPS format. You should describe the results of your first experiments here and what you wish to accomplish before the final presentation and paper submission. Note that, as with any conference, the page limits are strict! Papers over the limit will not be considered.
• Poster presentation (15 points): We will hold a poster session in the Atrium of the Paul Allen Center. Each team will be given a stand to present a poster summarizing the project motivation, methodology, and results. The poster session will give you a chance to show off the hard work you put into your project, and to learn about the projects of your peers. We will provide poster boards that are 32x40 inches. Both one large poster or several pinned pages are OK (fonts should be easily readable from 5 feet away).
• Project Report (60 points): Your write up should be 4 pages maximum (not including references) in Camera-ready NIPS format. You may have unlimited appendices for clarifications, however, no reviewer is required to look at these to evaluate the work. You should describe the task you solved, your approach, the algorithms, the results, and the conclusions of your analysis. Note that, as with any conference, the page limits are strict! Papers over the limit will not be considered.

Example project ideas can be found here.

## Homework

• Homework 0: Warm up (10 points)
• Due: 11:59 PM Thursday October 4
• Homework: PDF, LaTeX
• Homework 1: MLE, Bias-variance, Ridge Regression (25 points)
• Due: 11:59 PM Thursday October 18
• Homework: PDF, LaTeX
• Homework 2: Empirical Risk Minimization, Lasso, Logisitic regression (25 points)
• Homework 3: Bayesian inference, Kernel Regession, K-means, Matrix completion (25 points)
• Due: Tuesday November 20
• Homework: PDF, LaTeX, data for problem 5 (removed)
• Homework 4: EM, Convex programming, Neural networks (25 points)
• Due: Tuesday December 4
• Homework: PDF, LaTeX
• Homework 3, problem 5 revisited optional: (see assignment)

## Important Dates

Date Deliverable Due
10/4 Homework 0
10/18 Homework 1
10/25 Project proposal
11/1 Homework 2
11/15 Project milestone
11/20 Homework 3
12/4 Homework 4
12/4, 4:30-7:30 PM Poster presentation
12/7 Project report due
12/12, 4:30-7:30 PM Poster presentation
12/12 Optional Homework 3 revisited
12/14 Project Reviews due