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

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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 28 units, selected from the courses below, is required. At least 16 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 28 required units.

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

Required Foundational Proficiency

Pass digital signal processing proficiency test, or take EE 483 Introduction to Digital Signal Processing as a technical elective. (4 units)

EE 483 Introduction to Digital Signal Processing Units: 4

Foundations

Both courses are required (8 units):

EE 503 Probability for Electrical and Computer Engineers Units: 4
EE 510 Linear Algebra for Engineering Units: 4

Software

One course is required from this list (4 units).

CSCI 402 Operating Systems Units: 4
EE 455x Introduction to Programming Systems Design Units: 4
EE 595 Software Design and Optimization Units: 4

Learning and Data Analytics

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

EE 500 Neural Learning and Computational Intelligence Units: 4
EE 559 Mathematical Pattern Recognition Units: 3
EE 588 Optimization for the Information and Data Sciences Units: 4
EE 660 Machine Learning from Signals: Foundations and Methods Units: 3

Technical Electives

Take the remaining units from this list or from the Software, or Learning and Data Analytics lists. Courses my not apply to more than one degree requirement.

CSCI 544 Applied Natural Language Processing Units: 4
CSCI 585 Database Systems Units: 4
CSCI 677 Advanced Computer Vision Units: 4
EE 517 Statistics and Data Analysis for Engineers Units: 4
EE 519 Speech Recognition and Processing for Multimedia Units: 3
EE 542 Internet and Cloud Computing Units: 3
EE 546 Mathematics of High-Dimensional Data Units: 4
EE 556 Stochastic Systems and Reinforcement Learning Units: 4
EE 561 Foundations of Artificial Intelligence Units: 3
EE 562 Random Processes in Engineering Units: 4
EE 563 Estimation Theory Units: 3
EE 565 Information Theory and Its Application to (Big) Data Sciences Units: 4
EE 569 Introduction to Digital Image Processing 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 596 Wavelets and Graphs for Signal Processing and Machine Learning Units: 4
EE 619 Advanced Topics in Automatic Speech Recognition Units: 3
EE 669 Multimedia Data Compression Units: 3
ISE 538 Performance Analysis Using Markov Models Units: 3
MATH 541a Introduction to Mathematical Statistics Units: 3

Research

A maximum of 4 units of Directed Research or Thesis may be count 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