Princeton University, Spring 2022

**Prof. Ben Raphael** (OH: Wednesday 1:30-2:30pm, CS 309)

TA: **Daniel Melesse** (OH: Tuesday 3-4pm) Zoom

TA:**Alexander Strzalkowski ** (OH: Thursday 1-2pm; Tentative Location: COS Tea Room) Zoom

Lecture 01: Ben Raphael -- Mon, Wed 12:30-1:20pm, Friend Center 101.

Precept 02: Daniel Melesse -- Thu 10:00-10:50am

Precept 03: Alexander Strzalkowski -- Fri 12:30-1:20pm

- 22 January 2022: Website posted. Updates in progress.

Mon 24 January 2022
#### Introduction and Course Logistics

- [required] Reading: Course Syllabus
- [required] Reading: Lecture 1 Slides
- [optional] Sp21 Lecture by Ryan Adams: Introduction and Logistics (Stop at 6:16)

- [required] Reading: MML 2.0
- [optional] Sp21 Lecture by Ryan Adams: Vector Basics (15:03)
- [optional] Video: 3Blue1Brown on vectors

- [required] Reading: MML 2.2
- [optional] Sp21 Lecture by Ryan Adams: Matrix Basics (18:44)
- [optional] Video: 3Blue1Brown on matrix multiplication

Mon 31 January 2022

Assignment 1 **Due 11:59pm ET**

- [required] Reading: MML 2.1
- [required] Reading: MML 2.3
- [optional] Sp21 Lecture by Ryan Adams: Solving Linear Systems (36:44)

- [optional] Sp21 Lecture by Ryan Adams: Matrix Inversion (25:06)
- [optional] Blog post: Don't invert that matrix.

Wed 2 February 2022

- [required] MML 2.4
- [optional] Sp21 Lecture by Ryan Adams: Vector Spaces (16:11)

- [required] MML 2.5-2.6
- [optional] Sp21 Lecture by Ryan Adams: Linear Independence, Basis, and Rank (14:00)
- [optional] 3Blue1Brown video on basis vectors

Mon 7 February 2022

Assignment 2 **Due 11:59pm ET**

- [required] MML 2.7-2.8
- [required] Sp21 Lecture by Ryan Adams: Linear maps (9:35)
- [optional] 3Blue1Brown video on linear transformations and matrices

- [required] MML 2.7-2.8
- [optional] Sp21 Lecture by Ryan Adams: Change of Basis (11:00)
- [optional] 3Blue1Brown video on change of basis

Wed 9 February 2022

- [required] MML 3.0-3.3
- [optional] Sp21 Lecture by Ryan Adams: Norms and Inner Products (18:58)

- [required] MML 3.4-3.8
- [optional] Sp21 Lecture by Ryan Adams: Orthogonality and Projection (17:16)

Mon 14 February 2022

Assignment 3 **Due 11:59pm ET **

- [required] MML 3.4-3.8
- [optional] Sp21 Lecture by Ryan Adams: Gram-Schmidt Orthogonalization (15:15)
- [optional] F20 Lecture by Szymon Rusinkiewicz: Gram-Schmidt Orthogonalization

- [required] MML 4.0-4.1
- [optional] Sp21 Lecture by Ryan Adams: Matrix Invariants (12:28)
- [optional] F20 Lecture by Szymon Rusinkiewicz: Matrix Trace and Invariants
- [optional] 3Blue1Brown video on determinants

Wed 16 February 2022

- [required] MML 4.2
- [optional] Sp21 Lecture by Ryan Adams: Eigenvectors and Eigenvalues (26:10)
- [optional] 3Blue1Brown video on eigenvectors and eigenvalues

Mon 21 February 2022

Assignment 4 **Due 11:59pm ET **

- [optional] Sp21 Lecture by Ryan Adams: Modeling Data with Matrix Factorization (12:07)
- [required] MML 4.6-4.7
- [optional] F20 Lecture by Szymon Rusinkiewicz: LU and Cholesky Decomposition, Part I, Part II

Wed 23 February 2022

- [optional] Sp21 Lecture by Ryan Adams: SVD Basics (24:56)
- [required] MML 4.5
- [optional] F20 Lecture by Szymon Rusinkiewicz: Singular Value Decomposition

Mon 28 February 2022

Assignment 5 **Due 11:59pm ET**

- Catchup and review

Wed 2 March 2022

- MIDTERM
- No precepts on Thursday and Friday

- [optional] Sp21 Lecture by Ryan Adams: Why is Probability Important in Machine Learning (6:54)
- [optional] Sp21 Lecture by Ryan Adams: Probability Spaces and Random Variables (7:02)
- [required] MML 6.0

Wed 16 March 2022

- [optional] Sp21 Lecture by Ryan Adams: Probability Density and Mass Functions (6:56)
- [optional] Sp21 Lecture by Ryan Adams: Some Useful Probability Distributions (5:32)
- [required] MML 6.1-6.2
- [optional] Video: 3Blue1Brown on the Binomial Distribution

Mon 21 March 2022

- [optional] Sp21 Lecture by Ryan Adams: Basics of Joint Probability (6:53)
- [required] MML 6.3
- [optional] Video: 3Blue1Brown on Bayes' Theorem
- [optional] Metacademy: Bayesian Machine Learning Roadmap

Wed 23 March 2022

- [optional] Sp21 Lecture by Ryan Adams: Independence and Dependence (7:43)
- [required] MML 6.4

Mon 28 March 2022

- [optional] Sp21 Lecture by Ryan Adams: Useful Inequalities and Limit Theorems (13:58)

- [optional] Sp21 Lecture by Ryan Adams: The Gaussian Distribution (10:13)
- [required] MML 6.5

Wed 30 March 2022

Mon 4 April 2022

- [required] MML 6.5
- [optional] F20 Lecture by Szymon Rusinkiewicz: Monte Carlo Integration

Wed 6 April 2022

- [optional] Sp21 Lecture by Ryan Adams: Information Theory Basics (16:22)

- [required] MML 5.0-5.5
- [optional] Sp21 Lecture by Ryan Adams: Why is Differentiation Important to Machine Learning? (3:09)
- [optional] Sp21 Lecture by Ryan Adams: Differentiation Basics (4:44)
- [optional] Sp21 Lecture by Ryan Adams: Partial Derivatives (4:17)
- [optional] Sp21 Lecture by Ryan Adams: Best Affine Approximation (5:26)

Wed 13 April 2022

- [optional] Sp21 Lecture by Ryan Adams: Practical Differentiation (31:11)
- [optional] MML 5.6-5.8
- [optional] F20 Lecture by Szymon Rusinkiewicz: Differentiating Vector- and Matrix-Valued Functions

Mon 18 April 2022

- [required] MML 7.0-7.2
- [optional] Sp21 Lecture by Ryan Adams: Why is the Gradient the Direction of Steepest Ascent? (2:41)
- [optional] Sp21 Lecture by Ryan Adams: Optimization Basics (8:05)

Wed 20 April 2022

- [required] MML 7.3
- [optional] Sp21 Lecture by Ryan Adams: Convex Optimization (21:32)

- Assignment 1 -- Out Mon 24 Jan -- Due Mon 31 Jan at 11:59pm [hw1.pdf, Overleaf template, hw1.tex, cos302.cls, Gradescope submission, hw1-solutions.pdf]
- Assignment 2 -- Out Fri 28 Jan -- Due Mon 7 Feb at 11:59pm [hw2.pdf, Overleaf template, hw2.tex, cos302.cls, Gradescope submission, hw2-solutions.pdf]
- Assignment 3 -- Out Fri 4 Feb -- Due Mon 14 Feb at 11:59pm [hw3.pdf, Overleaf template, hw3.tex, cos302.cls, coords.pkl, mnist2000.pkl, Gradescope submission, hw3-solutions.pdf]
- Assignment 4 -- Out Fri 11 Feb -- Due Mon 21 Feb at 11:59pm [hw4.pdf, Overleaf template, hw4.tex, cos302.cls, mnist2000.pkl, Gradescope submission, hw4-solutions.pdf]
- Assignment 5 -- Out Fri 18 Feb -- Due Mon 28 Feb at 11:59pm [hw5.pdf, Overleaf template, hw5.tex, cos302.cls, nyt.pkl.gz, Gradescope submission]
- Assignment 6 -- Out Mon 14 Mar -- Due Mon 21 Mar at 11:59pm [hw6.pdf, Overleaf template, hw6.tex, cos302.cls, Gradescope submission]
- Assignment 7 -- Out Fri 18 Mar -- Due Mon 28 Mar at 11:59pm [hw7.pdf, Overleaf template, hw7.tex, cos302.cls, Gradescope submission]
- Assignment 8 -- Out Fri 25 Mar -- Due Mon 4 Apr at 11:59pm [hw8.pdf, Overleaf template, hw8.tex, cos302.cls, Gradescope submission]
- Assignment 9 -- Out Fri 1 Apr -- Due Mon 11 Apr at 11:59pm [hw9.pdf, Overleaf template, hw9.tex, cos302.cls, Gradescope submission]
- Assignment 10 -- Out Mon 11 Apr -- Due Wed 20 Apr at 11:59pm [hw10.pdf, Overleaf template, hw10.tex, cos302.cls, Gradescope submission]

- Assignments: 60% (lowest dropped)
- Midterm Exam: 20%
- Final Exam: 20%

**Does this course count towards****the SML certificate as a "Foundations of ML"?**No it does not. This is not a machine learning course in of itself. This course is intended to help you get the background to take machine learning and other courses that require continuous mathematics.**Does this course count towards the COS applications track?**No.**Can I take this concurrently with MAT 202?**Yes.**Are undergraduates and graduate students graded the same way?**Everyone's assignments and exams are graded with the same rubric, but the final grades will be curved separately.**Are precepts required?**No, but you should attend precept.**Why don't you do late days?**It introduces substantial additional bureaucracy and bookkeeping. I would rather that energy be spent working with students and making the course better.**What is Metacademy?**Metacademy is an exciting online tool developed by Roger Grosse and Colorado Reed for helping you to develop personalized instruction. It's meant to help you manage what you know about different topics and develop an individualized curriculum to learn a new subject.