Electrical and Computer Engineering (Machine Learning and Data Science) (MS)

Print Degree Planner (opens a new window) |

In addition to the general requirements of the Viterbi School of Engineering, the Master of Science in Electrical and Computer Engineering with an emphasis in Machine Learning and Data Science is also subject to the following requirements.

A minimum of 32 units, selected from the courses below, is required. At least 22 units must be taken in electrical engineering. Units to be transferred (maximum 4 with adviser approval) must have been taken prior to taking classes at USC – interruption of residency is not allowed. Curricular Practical Training units do not count toward the 32 required units.

For Admission Requirements, refer to USC Viterbi School of Engineering .

Required Foundational Proficiency

0-6 units required. Each of these must either be taken and used as a technical elective, or the respective proficiency test (digital signal processing, software) must be passed:

EE 483 Introduction to Digital Signal Processing Units: 4
EE 538 Computing Principles for Electrical Engineers Units: 2

Foundations

All courses are required (16 units)

EE 503 Probability for Electrical and Computer Engineers Units: 4
EE 510 Linear Algebra for Engineering Units: 4
EE 541 A Computational Introduction to Deep Learning Units: 2
EE 547 Applied and Cloud Computing for Electrical Engineers Units: 2
EE 559 Machine Learning I: Supervised Methods Units: 4

Learning and Data Analytics

Two courses are required from this list (6-8 units).

EE 546 Mathematics of High-Dimensional Data Units: 4
EE 556 Stochastic Systems and Reinforcement Learning Units: 4
EE 588 Optimization for the Information and Data Sciences Units: 4
EE 641 Deep Learning Systems Units: 2
EE 660 Machine Learning II: Mathematical Foundations and Methods Units: 4

Technical Electives

Take the remaining units from the following lists or from the Learning and Data Analytics list. Courses may not apply to more than one degree requirement.

Theory and Methods

CSCI 570 Analysis of Algorithms Units: 4
CSCI 585 Database Systems Units: 4
EE 517 Statistics and Data Analysis for Engineers Units: 4
EE 542 Internet and Cloud Computing Units: 3
EE 561 Foundations of Artificial Intelligence Units: 3
EE 562 Random Processes in Engineering Units: 4
EE 563 Estimation Theory Units: 3
EE 564 Digital Communication and Coding Systems Units: 4
EE 565 Information Theory and Its Application to (Big) Data Sciences Units: 4
EE 575 Computational Differential Geometry for Engineers Units: 3
EE 586L Advanced DSP Design Laboratory Units: 4
EE 592 Computational Methods for Inverse Problems Units: 4
EE 689 Computational Intelligence and Neural Learning Units: 4
ISE 538 Performance Analysis Using Markov Models Units: 3
MATH 541a Introduction to Mathematical Statistics Units: 3

Applications

CSCI 544 Applied Natural Language Processing Units: 4
CSCI 677 Advanced Computer Vision Units: 4
EE 519 Speech Recognition and Processing for Multimedia Units: 3
EE 569 Introduction to Digital Image Processing Units: 4
EE 619 Advanced Topics in Automatic Speech Recognition Units: 3
EE 669 Multimedia Data Compression Units: 3

Research

A maximum of 4 units of Directed Research or Thesis may be counted toward the Technical Electives area.

EE 590 Directed Research Units: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
EE 594a Master’s Thesis Units: 2
EE 594b Master’s Thesis Units: 2
EE 594z Master’s Thesis Units: 0