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Schedule

Stanford Root

Schedule

CS 168

The Modern Algorithmic Toolbox

UNITS:3-4
GRADING:Letter or Credit/No Credit
LEVEL:Undergrad
GER:—

This course will provide a rigorous and hands-on introduction to the central ideas and algorithms that constitute the core of the modern algorithms toolkit. Emphasis will be on understanding the high-level theoretical intuitions and principles underlying the algorithms we discuss, as well as developing a concrete understanding of when and how to implement and apply the algorithms. The course will be structured as a sequence of one-week investigations; each week will introduce one algorithmic idea, and discuss the motivation, theoretical underpinning, and practical applications of that algorithmic idea. Each topic will be accompanied by a mini-project in which students will be guided through a practical application of the ideas of the week. Topics include hashing, dimension reduction and LSH, boosting, linear programming, gradient descent, sampling and estimation, and an introduction to spectral techniques. Prerequisites: CS 107 and CS 161, or permission from the instructor.

Syllabus for selected term:
View Spring 2027 Syllabus

Sections

1 Term
Lecture 1Open
ID: 25751
0 / 999 enrolled
DAYS:TBD
TIME:TBD
LOCATION:TBD
units

CS 168: The Modern Algorithmic Toolbox

3-4 units · Letter or Credit/No Credit

This course will provide a rigorous and hands-on introduction to the central ideas and algorithms that constitute the core of the modern algorithms toolkit. Emphasis will be on understanding the high-level theoretical intuitions and principles underlying the algorithms we discuss, as well as developing a concrete understanding of when and how to implement and apply the algorithms. The course will be structured as a sequence of one-week investigations; each week will introduce one algorithmic idea, and discuss the motivation, theoretical underpinning, and practical applications of that algorithmic idea. Each topic will be accompanied by a mini-project in which students will be guided through a practical application of the ideas of the week. Topics include hashing, dimension reduction and LSH, boosting, linear programming, gradient descent, sampling and estimation, and an introduction to spectral techniques. Prerequisites: CS107 and CS161, or permission from the instructor.

Offered in Spring 2027 at Stanford University.

Spring 2027 sections

  • Lecture — TBA TBA (Undergrad)

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