Announcement

Welcome to CSE/STAT 416.

Course Description

It is hard to imagine anything more fascinating than automated systems that improve their own performance. The study of learning from data is commercially and scientifically important. This course is designed to provide a thorough grounding in the methodologies, technologies, and algorithms of machine learning. The topics of the course draw from classical statistics, from machine learning, from data mining, and from statistical algorithmics.

Students entering the class should have a pre-existing working knowledge of probability, statistics and algorithms, though the class has been designed to allow students with a strong numerate background to catch up and fully participate.

Prerequisite

• Either CSE143 (Computer Programming II) or CSE160 (Data Programming); and

• Either STAT311 (Elements of Statistical Methods), STAT/MATH390 (Statistical Methods in Engineering and Science), or STAT391 (Statistics of Data Science).

Instruction Times

• Lecture: Tuesday/Thursday 10:00-11:20, CSE2 G20

• TA Office Hours: see people

• Sewoong's Office hours: Thursday 11:30-12:30 in CSE2 207

• Quiz Sessions

  • AA Thursday 12:30-1:20, THO 125 (instructors: Anne and Jack)

  • AB Thursday 1:30-2:20, LOW 217 (instructors: Svet and Henry)

  • AC Thursday 2:30-3:20, CMU 120 (instructor: Anna)

  • AD Thursday 3:30-4:20, MGH 241 (instructors: Joshua and Zoheb)

Essential URLs

• The course mailing list is cse416-staff@cs.washington.edu. Please USE THE DISCUSSION BOARD (here) for non-personal issues. If you do have a personal issue, please use this list instead of emailing TA's directly. We'll be able to all see it and therefore respond faster.

• You should solve Concept Quizzes on Canvas here.

• You should submit Assignments on JupyterHub and solve corresponding quiz on Canvas here.

Calendar

Grading

• Assignments (60%)

• Concept Quizzes (15%)

• Final Exam (25%)

Textbooks (all optional)

• The Elements of Statistical Learning: Data Mining, Inference, and Prediction; Trevor Hastie, Robert Tibshirani, Jerome Friedman

• A Course in Machine Learning; Hal Duame

• Machine Learning: A Probabilistic Perspective; Kevin Murphy

• Pattern Recognition and Machine Learning; Chris Bishop