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

Samantha Graves

Student Services Manager

smgraves@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
  • Background material: background preparation guide — link TBD
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 517 Statistics and Data Analysis for Engineers 4
  • EE 542 Internet and Cloud Computing 4
  • EE 561 Foundations of Artificial Intelligence 4
  • 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
  • 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

Placement exams can substitute for EE 483 and EE 538. The DSP exam must be passed by the end of the first week of your second semester.

  • First semester — evaluate whether 8 or 10 units 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 guide — link TBD
  3. Decide how you will satisfy each proficiency — course, or a placement exam; the DSP exam 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 541 for a 10-unit start
  5. Bring schedule questions to Samantha Graves — Student Services, EEB 102E

Contacts and Resources

Program

Dr. Brandon Franzke

Senior Lecturer, ECE

Director, MS MLDS Program

EEB 504B · franzke@usc.edu

Student Services

Samantha Graves

Student Services Manager

EEB 102E · (213) 740-4447 · smgraves@usc.edu

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.