## CS 205L: Continuous Mathematical Methods with an Emphasis on Machine Learning

A survey of numerical approaches to the continuous mathematics used throughout computer science with an emphasis on machine and deep learning. Although motivated from the standpoint of machine learning, the course will focus on the underlying mathematical methods including computational linear algebra and optimization, as well as special topics such as automatic differentiation via backward propagation, momentum methods from ordinary differential equations, CNNs, RNNs, etc. Written homework assignments and (straightforward) quizzes focus on various concepts; additionally, students can opt in to a series of programming assignments geared towards neural network creation, training, and inference. (Replaces
CS205A, and satisfies all similar requirements.) Prerequisites:
Math 51; Math104 or MATH113 or equivalent or comfort with the associated material.

Terms: Win
| Units: 3

Instructors:
Fedkiw, R. (PI)
;
Becker, N. (TA)
;
He, H. (TA)
;
Jobalia, S. (TA)
;
Kuang, Z. (TA)
;
Li, J. (TA)
;
Li, K. (TA)
;
Malde, R. (TA)
;
Omens, D. (TA)
;
Pradeep, G. (TA)
;
Uy, M. (TA)
;
Yan, B. (TA)

## MATH 113: Linear Algebra and Matrix Theory

Algebraic properties of matrices and their interpretation in geometric terms. The relationship between the algebraic and geometric points of view and matters fundamental to the study and solution of linear equations. Topics: linear equations, vector spaces, linear dependence, bases and coordinate systems; linear transformations and matrices; similarity; dual space and dual basis; eigenvectors and eigenvalues; diagonalization. Includes an introduction to proof-writing. (
Math 104 offers a more application-oriented treatment.) Prerequisites:
Math 51

Terms: Aut, Win, Spr
| Units: 4
| UG Reqs: GER:DB-Math, WAY-FR

Instructors:
Malinnikova, E. (PI)
;
Swaminathan, M. (PI)
;
Vondrak, J. (PI)
...
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Instructors:
Malinnikova, E. (PI)
;
Swaminathan, M. (PI)
;
Vondrak, J. (PI)
;
Amar, S. (TA)
;
Niu, J. (TA)

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