Master of Science · Electrical and Computer Engineering

Machine Learning and Data Science

Information Session for New Students

2026/2027 Entry

Dr. Brandon Franzke

Senior Lecturer, ECE

Director, MS MLDS Program

franzke@usc.edu

Thursday, 8/20/2026

Program Philosophy and Preparation

Philosophy

  • Depth on core MLDS topics
  • Breadth across theory, methods, and applications
  • Long-term and short-term time horizons
  • Created to enable you to start and thrive in a career that includes MLDS as a major component

Preparation

  • All admitted students have been judged to have appropriate background for the degree
  • MLDS requires a breadth of technical knowledge — review or fill in background material early
Your background engineering · mathematics · computing Mathematics EE 503 Probability EE 510 Linear Algebra Machine Learning EE 541 Intro Deep Learning EE 559 Machine Learning I Computing EE 538 Computing Principles EE 547 Cloud Computing foundations — required of everyone Depth — Set 2 learning and data analytics choose 2 of 5 Breadth — Set 3 technical electives theory · applications · research Your future industry practice · PhD study

MLDS vs the Standard MS EE

MLDS specifies most of the program; the standard MS EE leaves course choice to you and your adviser.

MS ECE (MLDS) 32 units

  • Prescribed foundation: 5 courses, 16 units — all required
  • Two of five Set 2 courses required
  • Electives from a defined list — CSCI, ISE, and MATH options pre-approved
  • Two proficiency requirements: DSP and computing

MS EE (standard) 28 units

  • No fixed core — flowchart guidance, adviser-approved electives
  • Every non-EE course requires written adviser approval
  • At least 20 units in EE; at most 12 units at the 400 level
  • No proficiency requirements

Both programs: minimum 3.0 GPA · at most 4 units of EE 590 Directed Research · at most 4 transfer units

Degree Requirements

Foundational Proficiency DSP and computing — both required counts toward Set 3

Set 1 · Foundations five courses — all required 16 units

Set 2 · Learning and Data Analytics choose two of five 6–8 units

Set 3 · Technical Electives choose from the approved list 8–10 units

Set 1 · 16

Set 2 · 6–8

Set 3 · 8–10

= 32 units

Requirements of record: USC Catalogue 2026–27 — MS ECE (Machine Learning and Data Science)

Course Requirements

Foundational Proficiency — both required counts toward Set 3

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

Set 1 — Foundations all required · 16 units

  • EE 503 Probability for Electrical and Computer Engineers 4
  • EE 510 Linear Algebra for Engineering 4
  • EE 541 A Computational Introduction to Deep Learning 2 co-req: EE 503 and EE 510
  • EE 547 Applied and Cloud Computing for Electrical Engineers 2 pre-req: EE 538
  • EE 559 Machine Learning I: Supervised Methods 4 co-req: EE 503 and EE 510

Set 2 — Learning & Data Analytics choose 2 · 6–8

  • EE 546 Mathematics of High Dimensional Data 4 pre-req: EE 503 and EE 510
  • EE 556 Stochastic Systems and Reinforcement Learning 4 pre-req: EE 503
  • EE 588 Optimization for Information & Data Sciences 4 pre-req: EE 510
  • EE 641 Deep Learning Systems 2 pre-req: EE 541 and EE 559
  • EE 660 Machine Learning II: Foundations & Methods 4 pre-req: EE 503, EE 510, and EE 559

Prerequisites

EE 503 and EE 510 come first — nearly every later course requires them.

Set 1 Set 2 proficiency prerequisite corequisite EE 503 (4) Probability EE 510 (4) Linear Algebra EE 538 (2) Computing Principles EE 541 (2) Intro to Deep Learning EE 559 (4) Machine Learning I EE 546 (4) High-Dimensional Data EE 556 (4) Stochastic Systems & RL EE 588 (4) Optimization EE 547 (2) Cloud Computing EE 641 (2) Deep Learning Systems EE 660 (4) Machine Learning II also requires EE 503, EE 510

Set 3 — Technical Electives

8–10 units: the remainder of the 32 after Sets 1 and 2. Any additional Set 2 course also counts.

Theory and methods

  • CSCI 570 Analysis of Algorithms 4
  • CSCI 585 Database Systems 4
  • EE 508 Hardware Foundations of Machine Learning 4
  • EE 517 Statistics and Data Analysis for Engineers 4
  • EE 542 Internet and Cloud Computing 4
  • EE 561 Foundations of Artificial Intelligence 3
  • EE 562 Random Processes in Engineering 4
  • EE 563 Inference and Estimation: Theory and Algorithms 4
  • EE 564 Digital Communications and Coding Systems 4
  • EE 565 Information Theory & Applications to Data Sciences 4
  • EE 575 Computational Differential Geometry for Engineers 4
  • EE 586L Advanced DSP Design Laboratory 4
  • EE 592 Computational Methods for Inverse Problems 4
  • EE 596 Wavelets & Graphs for Signal Processing & ML 4
  • EE 689 Computational Intelligence and Neural Learning 4
  • ISE 538 Markov Models for Performance Analysis 4
  • ISE 558 Fundamentals of Data Engineering 4
  • MATH 541a Introduction to Mathematical Statistics 3

Applications

  • CSCI 544 Applied Natural Language Processing 4
  • CSCI 677 Advanced Computer Vision 4
  • EE 519 Speech Recognition and Processing for Multimedia 3
  • EE 569 Introduction to Digital Image Processing 4
  • EE 619 Advanced Topics in Automatic Speech Recognition 3
  • EE 669 Multimedia Data Compression 4
  • EE 682 Law, AI and Intellectual Property for Engineers 4

Research — 4 units max

  • EE 590 Directed Research 1–4
  • EE 594ab Master’s Thesis 4

Foundational proficiency

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

Example Schedules — Four Semesters

Plan A — two Set 2 courses in the third semester

Semester 1 (Fall) Semester 2 (Spring) Semester 3 (Fall) Semester 4 (Spring)
EE 503 Probability (4) EE 538 Computing Principles (2) EE 547 Applied and Cloud Computing (2) Set 3 electives (2–4)
EE 510 Linear Algebra (4) EE 559 Machine Learning I (4) Set 2 — two courses (6–8)
EE 541 Computational Intro to Deep Learning (2) EE 483 Digital Signal Processing (4)
10 units 10 units 8–10 units 2–4 units

Plan B — one Set 2 course plus EE 641

Semester 1 (Fall) Semester 2 (Spring) Semester 3 (Fall) Semester 4 (Spring)
EE 503 Probability (4) EE 538 Computing Principles (2) EE 547 Applied and Cloud Computing (2) Set 3 electives (4)
EE 510 Linear Algebra (4) EE 559 Machine Learning I (4) Set 2 — one course (4)
EE 541 Computational Intro to Deep Learning (2) EE 483 Digital Signal Processing (4) EE 641 Deep Learning Systems (2)
10 units 10 units 8 units 4 units

Planning Notes

Proficiency tests can substitute for EE 483 and EE 538. The DSP test must be passed by the end of the first week of your second semester.

  • First semester — evaluate whether 8 units (EE 503 and EE 510) or 10 (add EE 538 or EE 541) is best for you
  • Course order — take Foundational Proficiency and Set 1 courses early; most later courses require them
  • Choosing Set 2 — pick the pair that matches your direction
    • EE 660 — mathematical foundations; a natural fit if you lean theory or a PhD
    • EE 641 — building and deploying deep learning systems
    • EE 546 — high-dimensional data methods
    • EE 588 — optimization; control and robotics
  • Set 2 timing — EE 641 is offered in fall only; choose your two Set 2 courses a semester ahead
  • Project load — EE 541, EE 559, EE 547, and EE 641 have substantial projects
    • Three classes with large projects in the same semester is probably too many
    • Check course syllabi or ask the instructor

Example Schedule — Five Semesters with Summer

8 units in the first semester; EE 559 in summer¹

Semester 1 (Fall) Semester 2 (Spring) Semester 3 (Summer) Semester 4 (Fall) Semester 5 (Spring)
EE 503 Probability (4) EE 538 Computing Principles (2) EE 559 Machine Learning I (4)¹ EE 547 Applied and Cloud Computing (2) Set 3 electives (4)
EE 541 Computational Intro to Deep Learning (2) Set 2 — one course (4)
EE 510 Linear Algebra (4) EE 483 Digital Signal Processing (4) EE 641 Deep Learning Systems (2)
8 units 8 units 4 units 8 units 4 units

¹ subject to EE 559 being offered in summer — confirm before planning on it

Careers in Industry

Roles this degree prepares you for — and where that preparation happens.

Machine learning engineer build, train, and ship models in production EE 541 · 559 · 641 · 547

Data / applied scientist statistical modeling, experiments, decisions from data EE 503 · 559 · 660

ML infrastructure engineer data pipelines, serving, and compute at scale EE 547 · 542

Research engineer implement new methods — industry research labs Set 2 · EE 590

Research Opportunities

The department runs more than seventy research centers and labs; these areas are closest to MLDS.

Research areas

  • Communications, information theory, and machine learning
  • Signal and image processing
  • Control, autonomy, and intelligent systems
  • Computer engineering and systems for ML

Centers and institutes

Getting involved

  • Research conversations usually start in a course — talk to your instructors
  • EE 590 Directed Research — by request after your first semester; up to 4 units count toward Set 3
  • EE 594ab Master’s Thesis — the larger commitment

After your MS

If you start your career after the MS

  • Learn the material in your courses
  • Improve your software skills — use them in classes and in your own projects
  • Explore, study, and practice MLDS on your own
  • Consider internships and research projects
    • Internships during the program: check CPT eligibility and timing with Student Services and OIS
  • Develop contacts: peers, industry, faculty

If you start a PhD after the MS

  • Do very well in your courses
  • Develop contacts with faculty
  • Participate in research projects
    • EE 590 Directed Research possible after your first semester, by request — not guaranteed

Getting Started

  1. Accept your Ed Discussion invitation — sent to your USC email
  2. Review the background preparation posts
  3. Decide how you will satisfy each proficiency — course, or a proficiency test; the DSP test must be passed by the end of the first week of your second semester
  4. Check your first-semester enrollment — EE 503 and EE 510, plus EE 538 or EE 541 for a 10-unit start
  5. Bring schedule questions to your assigned ECE advisor — assignments by last name

Contacts and Resources

Program

Dr. Brandon Franzke

Senior Lecturer, ECE

Director, MS MLDS Program

EEB 504B · franzke@usc.edu

ECE Advising — by last name

A–J — Patty Rinehart · ptrinida@usc.edu

K–L — Gabby Garcia · gabby.garcia@usc.edu

M–T — Jaime Zelada · zelada@usc.edu

U–Z — Vianey Villaseñor · vgvillas@usc.edu

Your advisor is the first stop — they coordinate with the program on exceptions and substitutions.

Proficiency courses

Resources

Discussion

Comments and advice from the faculty and staff present

Questions and comments from students

And hopefully some answers

Slides from this session will be posted.