The Intelligent Systems Program is a multidisciplinary graduate program at the University of Pittsburgh dedicated to applied artificial intelligence (AI). Many of Pitt’s acclaimed schools are represented through our associated faculty, including the School of Medicine, the School of Law, the School of Education, the Swanson School of Engineering, and the Kenneth P. Dietrich School of Arts and Sciences.
What Do We Offer?
- Broadly interdisciplinary approach: We offer a strong, well-balanced foundation in the fundamentals of AI and many opportunities for advanced research and training in many disciplines, including computer science, biomedical informatics, cognitive psychology, information science, education, law, and more.
- Focused, customized curricula: Building on the core curriculum, you will design your own personalized curricula that will prepare you for interdisciplinary research in your areas of interest.
- Collaborative atmosphere: Faculty members and students present their research in regular program seminars, which exposes you to a broad range of research topics and methods, affording you the opportunity to present your own research.
- Highly motivated faculty: Pitt’s widely published ISP faculty are leaders in their fields. Drawing on the strengths of diverse sectors of the university, and participating in over 30 funded research projects, they support graduate students through collaborative research, personal mentoring, and external research funding.
Degree Requirements
Students pursuing the Master of Science degree in ISP must adhere to the SCI requirements for graduation and complete a minimum of 30 credits as outlined below, as well as an MS Project. Students must also earn a grade of B- or better in each of the courses in the appropriate ISP curriculum (the general track or the Biomedical Informatics track) and maintain a GPA of at least 3.0.
General Track Curriculum
First-year students are encouraged but not required to take:
- ISSP 2020 - TOPICS IN INTELLIGENT SYSTEMS
- INFSCI 3005 - INTRODUCTION TO THE DOCTORAL PROGRAM
- ISSP 2030 - ADVANCED TOPICS IN INTELLGENT SYSTEMS
Core
AND Choose Two of the Following:
- ISSP 2170 / CS 2750 - MACHINE LEARNING
- ISSP 2230 / CS 2731 - INTRO NATURAL LANGUAGE PROCSSNG
- ISSP 2180 / CS 2770 - COMPUTER VISION
Theory
Applied or mathematical statistics. Choose one of the following:
- BIOST 2041 - INTRODUCTION TO STATISTICAL METHODS 1
- BIOST 2042 - INTRODUCTION TO STATISTICAL METHODS 2
- BIOINF 2118 - STATISTICAL FOUNDATIONS OF BIOMEDICAL INFORMATICS
- STAT 2131 - APPLIED STATISTICAL METHODS 1
- STAT 2132 - APPLIED STATISTICAL METHODS 2
Theory of computation, algorithms. Choose one of the following:
One additional course required. Any of the theory courses listed above are acceptable.
Advanced courses
Four ISSP advanced lecture courses, numbered 2000 or higher and approved by the academic adviser.
Biomedical Informatics Track (ISP/MI)
This assumes that a student already has training in a health care field; if this is not so, then the faculty will select a set of courses that teach the student basic medical knowledge, and the student may take these courses as electives.
First-year students are encouraged but not required to take:
- ISSP 2020 - TOPICS IN INTELLIGENT SYSTEMS
- INFSCI 3005 - INTRODUCTION TO THE DOCTORAL PROGRAM
- ISSP 2030 - ADVANCED TOPICS IN INTELLGENT SYSTEMS
Core
- ISSP 2083 / BIOINF 2032 - BIOMEDICAL INFORMATICS JOURNAL CLUB
- ISSP 2016 / BIOINF 2070 - FOUNDATIONS OF BIOMEDICAL INFORMATICS 1
- ISSP 2160 / CS 2710 - FOUNDATIONS OF ARTIFICIAL INTELLGENCE
Then choose;
One of the following:
- ISSP 2170 / CS 2750 - MACHINE LEARNING
- ISSP 2230 / CS 2731 - INTRO NATURAL LANGUAGE PROCSSNG
- ISSP 2180 / CS 2770 - COMPUTER VISION
AND choose one of the following:
- CS 1510 - ALGORITHM DESIGN
- CS 2150 - DESIGN & ANALYSIS OF ALGORITHMS
- CS 3150 - ADV TOPCS DSGN & ANALYS ALGORTHM
AND choose one of the following:
- BIOST 2041 - INTRODUCTION TO STATISTICAL METHODS 1
- BIOST 2042 - INTRODUCTION TO STATISTICAL METHODS 2
- BIOINF 2118 - STATISTICAL FOUNDATIONS OF BIOMEDICAL INFORMATICS
- STAT 2131 - APPLIED STATISTICAL METHODS 1
- STAT 2132 - APPLIED STATISTICAL METHODS 2
AND choose two of the following:
- ISSP 2070 / BIOINF 2101 - PROBABILISTIC METHODS
- ISSP 2017 / BIOINF 2071 - FOUNDATIONS OF BIOMEDICAL INFORMATICS 2
- ISSP 2240 / INFSCI 2130 - DECISION ANALYSIS AND DECISION SUPPORT SYSTEMS
- BIOINF 2121 - HUMAN-COMPUTER INTERACTION AND EVALUATION METHODS
- BIOINF 2117 - APPLIED MEDICAL INFORMATICS
- BIOINF 2016 - FOUNDATIONS OF TRANSLATIONAL INFORMATICS
- BIOINF 2124 - PRINCIPLES OF GLOBAL HEALTH INFORMATICS
Three (3) Graduate level (2000 or higher, three (3) credits) ISSP lecture courses that have been approved by your advisor as being relevant to your studies in the ISP.