Electives for PhD Students-Machine Learning Department - Carnegie Mellon University

Electives

ML PhD students:
One elective or courses combined for a total of 12 units must be chosen from Statistics.
You must also have a research depth of 24 units.

Both Statistics and Tepper offer "mini" half-term courses. Two such "mini" courses are equivalent to one (12 unit) graduate course.

Suggested Electives from Statistics

36-707 Regression Analysis
36-752 Adv. Probability Overview
36-754 Adv. Probability
36-755 Advanced Statistical Theory I
36-759 Statistical Models of the Brain

MINI Courses (must take two for a total of 12 units)
36-715 Discrete Time Stochastic Processes, 6 units
36-716 Continuous Time Stochastic Processes, 6 units
36-720 Discrete Multivariate Analysis, 6 units
36-722 Applied Continuous Multivariate Analysis, 6 units
36-724 Applied Bayesian and Computational Methods, 6 units
36-728 Time Series, 6 units
36-729 Observational Causal Inference, 6 units
36-737 Applied Multilevel/Longitudinal Models, 6 units
36-738 Advanced Statistical Methods, 6 units
36-739 Topics in applied Statistics, 6 units
36-762 Data Privacy, 6 units
36-763 Hierarchical Models, 6 units
36-769 Nonparametric Statistical Inference, 6 units
36-770 Nonparametric Methods, 6 units
36-794 Astrostatistics, 6 units

Suggested Depth Requirement Electives from SCS

Al:
10-703 Deep Reinforcement Learning & Control
10-725 Convex Optimization
10-708 Probablistic Graphical Models
10-806 Foundations of Machine Learning & Data Science
10-807 Topics in Deep Learning
15-780 Graduate Artificial Intelligence
15-857 Analytical Performance Modeling & Design of Computer Systems
15-859 (M) Randomized Algorithms
15-887 Planning, Execution, and Learning
15-896 Truth, Justice and Algorithms

Algorithms & Theory:
10-725 Convex Optimization
10-806 Foundations of Machine Learning & Data Science
15-855 Computational Complexity Theory
15-857 Analytical Performance Modeling & Design of Computer Systems
15-859 Special Topics in Theory - check for appropriate topics
15-859 (B) Machine Learning Theory
15-859 (E) Special Topics in Theory: Advanced Algorithms
15-896 Truth, Justice and Algorithms
16-811 Mathematical Fundamentals for Robotics
21-801 Adv. Topics Discrete Math (Random Graphs)

Computational Biology:
02-750 Automation of Biological Research: Robotics & Machine Learning
02-710 Computational Genomics
02-717 Algorithms in Nature
02-718 Computational Medicine
02-740 Bioimage Informatics
10-708 Probablistic Graphical Models

Computer Vision:
10-725 Convex Optimization
10-807 Topics in Deep Learning
16-720 Computer Vision
16-822 Geometry-Based Methods in Vision
16-824 Learning Based Methods in Vision

Databases:
15-823 Advanced Database Topics

NLP or Text Analysis:
10-708 Probablistic Graphical Models
10-710/11-763 Structured Prediction for Language & Other Discrete Data
10-802/11-772 Analysis of Social Media
11-711 Algorithms for NLP
11-727 Computational Semantics for NLP
11-741 Machine Learning for Text Mining
11-744 Experimental Information Retrieval
11-745 Advanced Statistical Learning Seminar (6 units)
11-761 Language and Statistics
11-762 Language and Statistics II
11-773 Text-Driven Forecasting

Robotics:
02-750 Automation of Biological Research: Robotics & Machine Learning
15-887 Planning, Execution, and Learning
16-811 Mathematical Fundamentals for Robotics
16-831 Statistical Techniques in Robotics
16-899C Adaptive Control and Reinforcement Learning

Other electives from SCS approved but don't have a Depth Requirement category:
10-704 Information Processing & Learning
11-745 Adv. Statistical Learning Seminar (6 units)
11-755 Machine Learning for Signal Processing
15-830 Computational Methods in Sustainable Energy
18-755 Networks in the Real World

Suggested Depth Requirement Electives for CNBC Track

03-762 Advanced Cellular Neuroscience
03-763 Systems Neuroscience
15-883 Computational Models of Neural Systems
36-759 Statistical Models of the Brain
85-719 Introduction to Parallel Distributed Processing
85-765 Cognitive Neuroscience (12 units)

Applicable Courses from the University of Pittsburgh (Please see http://www.cmu.edu/hub/registration/undergraduates/cross/outgoing.html)
NROSCI 2100 Cellular & Molecular Neurobiology
NROSCI 2102/2103 Systems Neurobiology
MATH 3375 Computational Neuroscience

Suggested Concentration Electives from School of Public Policy & Management:

10-830/90-904 Research Seminar in Machine Learning & Policy, 6 units, A3 mini
10-831/90-921 Special topics in Machine Learning & Policy, 6 units, A4 mini

Suggested Concentration Electives from Tepper (Must follow Tepper special registration rules)

Finance Track:
45-814 Options
46-926 Linear Models/Equity Portfolio Management
46-929 Financial Time Series Analysis
46-944 Stochastc Calc Fin 1
46-945 Stochastic Calculus II

Marketing Track:
15-892 Foundations of Electronic Marketplaces (CS course)
47-800 Intermediate Microeconomic Analysis
47-741 Seminar in Marketing I
47-742 Seminar in Marketing II
47-743 Seminar in Marketing III
47-744 Analytical and Structural Marketing Models
45-821 Marketing with Electronic & Social Media
45-824 Database Marketing

Information Systems Track:
47-800 Intermediate Microeconomic Analysis
45-870 Management of Information Systems
45-871 Information Strategy, Systems and Economics
47-951 Seminar in Information Systems I
47-952 Seminar in Information Systems II
47-953 Seminar in Information Systems III
47-954 Seminar in Information Systems IV

NOTE: Tepper courses are on the mini-system.45-* and 46-* are Master level courses and the 47-* are PhD level courses Suggested

Concentration Electives from Philosophy

80-605 Rational Choice
80-614 Logic in Artificial Intelligence
80-616 Probability and Artificial Intelligence
80-621 Causality in the Social Sciences