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About

Table of contents

  1. About
  2. Lecture
  3. Grading
  4. Course Staff

About

Forecasting has been used to predict elections, climate change, and the spread of COVID-19. Poor forecasts led to the 2008 financial crisis. In our daily lives, good forecasting ability can help us plan our work, be on time to events, and make informed career decisions. This practically-oriented class will provide you with tools to make good forecasts, including Fermi estimates, calibration training, base rates, scope sensitivity, and power laws. We’ll discuss several historical instances of successful and unsuccessful forecasts, and practice making forecasts about our own lives, about current events, and about scientific progress.

Prerequisites: Stat134 or a similar probability course (i.e. EECS126, STAT140, IEOR172).

Lecture

MWF11-12, in 3108 Etcheverry Hall

This class will be heavily disussion-based and attendance will count towards the grade. Monday and Wednesday lectures will be a combination of traditional lecture and group activities, while Friday will be student-led small group discussions with instructors helping to facilitate.

Instead of exams, there will be a final project. Students in Stat260 will be expected to do a more substantial project.

There will be no official lab / discussion block, but some homework will involve programming and we will have GSIs available during the lab slot to help students debug.

Grading

Grades will be based on a combination of:

  • Lecture attendance (15%)
  • Weekly forecasting exercises (20%)
  • Homework (35%)
  • Final project (30%)

Course Staff

To reach course staff, you can email forecasting-class-staff@lists.berkeley.edu. If possible, please avoid emailing professors or GSIs directly!

Jacob Steinhardt

Lead Instructor

(email)

Danny Hernandez

Instructor

(email)

Yan Zhang

Instructor

(email)