Section 6
CS229: Machine Learning
Autumn 2019
Instructor
Course Description This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
Announcements
- 09/19/19 Welcome to CS229! We look forward to meeting you on Monday 9/23 9:30am at NVIDIA Auditorium!
Course Information
- Time and Location
- MW 9:30 AM - 10:50 AM, NVIDIA Auditorium
- Contact and Communication
- Piazza is the forum for the class.
- All official announcements and communication will happen over Piazza.
- Any questions regarding course content and course organization should be posted on Piazza. You are strongly encouraged to answer other students' questions when you know the answer.
- If there are private matters specific to you (e.g special accommodations, requesting alternative arrangements etc.), please create a private post on Piazza.
- For longer discussions with TAs and to get help in person, please attend office hours.
- Answers to commonly asked questions and clarifications to the homeworks will be posted on the FAQ.
- Office Hours and Course Calendar
- TA office hours and the course calendar can be found here.
- Teaching Assistants
- FAQ
- Answers to frequently asked questions can be found here.
Logistics
- Prerequisites
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Students are expected to have the following background:
- Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program in Python/numpy.
- Familiarity with probability theory (CS 109 or STATS 116)
- Familiarity with multivariable calculus and linear algebra (MATH 51)
- Optional Friday Lectures
- To review material from the prerequisites or to supplement the lecture material, additional lectures will be held every Friday in Gates B01, 12:30PM - 1:20PM (weeks 1-9). Attendance to these lectures is optional, but encouraged.
- Optional Discussion Sections
- In addition to the regular lectures and optional friday lecture, there will also be optional weekly discussion sections led by TAs. These sessions are meant to be interactive and in a small, traditional classroom setting. They will largely involve working through problems that are similar to the homeworks. Details for how to signup for these sections will be published in a Piazza announcement during week 1.
- Course Materials
- There is no required text for this course. Notes will be posted periodically on the class syllabus.
- Grading
- There will be four written homeworks, one midterm, and a major open-ended term project (see the projects page for details). The assignments will contain written questions and questions that
require some Python programming. In the term project, you will investigate some interesting aspect of machine learning or apply machine learning to a problem that interests you.
We try very hard to make questions unambiguous, but some ambiguities may remain. Ask if confused or state your assumptions explicitly. Reasonable assumptions will be accepted in case of ambiguous questions.
Course grades: will be based 40% on homeworks (10% each), 20% on the midterm, and 40% on the major term project. - Submitting Assignments
- Assignments will be submitted through Gradescope. You should have received an invite to Gradescope for CS229 Machine Learning Fall 2019. If you have not received an invite email, first log in to Gradescope with your @stanford.edu email and see whether you find the course listed, if not please post a private message on Piazza for us to add you.
- Late Assignments
- Each student will have a total of eight free late (calendar) days to use for homeworks, project proposals and project milestones. Once these late days are exhausted, any assignments turned in late will be penalized 20% per late day. However, no assignment will be accepted more than three days after its due date, and late days cannot be used for the final project poster or write-up. Each 24 hours or part thereof that a homework is late uses up one full late day.
- Honor Code
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We strongly encourage students to form study groups. Students may discuss and work on homework problems in groups. However, each student must write down the solutions independently, and without referring to written notes from the joint session. In other words, each student must understand the solution well enough in order to reconstruct it by him/herself. In addition, each student should write on the problem set the set of people with whom s/he collaborated.
Further, since we occasionally reuse problem set questions from previous years, we expect students not to copy, refer to, or look at the solutions in preparing their answers. It is an honor code violation to intentionally refer to a previous year's solutions. This applies both to the official solutions and to solutions that you or someone else may have written up in a previous year.
- Lecture Video Policy
- Video cameras located in the back of the room will capture the instructor presentations in this course. For your convenience, you can access these recordings by logging into the course Canvas site. These recordings might be reused in other Stanford courses, viewed by other Stanford students, faculty, or staff, or used for other education and research purposes. Note that while the cameras are positioned with the intention of recording only the instructor, occasionally a part of your image or voice might be incidentally captured. If you have questions, please contact a member of the teaching team.
