Notes

Course Evaluation

Grading breakdown

• There will be four homework assignments worth 8% each. Your lowest grade will be dropped, so that 4 homework assignments = 24%
• There will be a (take-home) midterm in week 6, worth 26%
• One assignment on recommender systems (after week 5), worth 25%
• A short open-ended assignment, worth 25%

Assignment 1 counts as Comprehensive Exam, pass if > 15/25

Grading breakdown

HW = 24% Midterm = 26% Assignment 1 = 25% Assignment 2 = 25%

Actual goals:

• Understand the basics and get comfortable working with data and tools (HW)
• Comprehend the foundational material and the motivation behind different techniques (Midterm)
• Build something that actually works (Assignment 1)
• Apply your knowledge creatively (Assignment 2)

Homeworks

• Homework should be delivered by the beginning of the Monday lecture in the week that it’s due
• All submissions will be made electronically (instructions will be in the homework spec, on the class webpage)

Schedule

Schedule (subject to change but hopefully not):

• Week 1: Hw 1 out
• Week 3: Hw 1 due, Hw2 out
• Week 5: Hw 2 due, Hw3 out, Assign. 1 out Week 6: midterm
• Week 7: Hw 3 due, Hw4 out, Assign. 2 out Week 8: Assignment 1 due
• Week 9: Hw4 due
• Week 10: Assignment 2 due

Annotated Slides

Introduction & Course Outline

Regression