Education-Wayne AI Research

AI concentration highlights the follows:

The breadth requirement as outlined in the current WSU Graduate Bulletin , i.e. at least one course from each of three general areas: (1) theory and algorithms (2) computer and network systems and (3) information and intelligent systems.
The depth requirement for AI concentration: at least four lecture courses must be selected from the following list and a course can used to fulfill both breadth and depth requirements.
The AI concentration is only available to Plan C master students.
All other M.S. degree requirements remain unchanged in the current proposal.

AI elective courses (take no less than four lecture courses to fulfill the depth requirement)

CSC 5430 Game Programming and Design I
CSC 5800 Intelligent Systems: Algorithms and Tools
CSC 5825 Introduction to Machine Learning and Applications
CSC 5870 Computer Graphics I
CSC 6430 Game Programming and Design II
CSC 6800 Artificial Intelligence I
CSC 6860 Digital Image Processing and Analysis
CSC 6870 Computer Graphics II
CSC 7800 Artificial Intelligence II
CSC 7810 Data Mining: Algorithms and Applications
CSC 7825 Machine Learning
CSC 7860 Computer Vision

Rationale for Proposed Revisions:

Depth and Breadth Requirements:

The strong demand for high-level AI talent from market. One quick look at the job market reveals a startling revelation: there are dearth of high-level AI experts, which is mostly tied to the fact that companies have started to define a more concrete set of requirements other than those can be fulfilled using the off-the-shelf techniques. At the same time, it looks like not enough people are taking rigorous AI courses for this line of work.
Although there are many master programs in AI already, most of them are multi-disciplinary with an emphasis on the applications using standard AI techniques. Consequently, few of them are designed to train high-level AI experts in developing AI algorithmic and computing infrastructures to enable breakthrough in the wide adoption of AI techniques.
Addition of the depth requirement to the breath requirement are expected to encourage computer science graduate students to focus more on AI related courses, honing their AI technical skills and enhancing their competition in the job market.
Addition of the depth requirement to the breath requirement can be used as a template for creating other CS concentrations to better fit in the need from market.

Effective Date for Revisions:

Accommodations for Current CS M.S. Students:

No special accommodations are required. A current student can either fulfill the existing breadth requirement of 30 credits or switch to the AI concentration by fulfilling both breadth and depth requirements.

Current M.S. Program Mission Statement:

The mission of the M.S. program at the Department of Computer Science at Wayne State University is to instill in our students a strong theoretical background in computer science, to expose them to the various rich disciplines in computer science, and to develop an appreciation for state of the art research. Students build their theoretical background via our core courses of algorithms and automata. Furthermore, students are able to broaden their perspectives by choosing courses from a variety of computer science disciplines including artificial intelligence, bioinformatics, computer game design, computer graphics, computer networking, computer vision, data mining, database management systems, parallel and distributed systems, real-time and embedded systems, and software engineering. Finally, our graduate seminars invite experts from across the country giving our students a taste for state of the art research. By the end of our program, our students are well equipped and able to pursue further Ph.D. studies as well as careers in industry, government and other organizations. No changes are being made to the program mission statement in the proposed program revisions.

Current M.S. Program Learning Objectives:

The program learning objectives (PLOs) for the CS M.S. Program is provided for reference:

An ability to analyze a problem, and identify and define the computing requirements appropriate to its solution, and produce a corresponding implementation.
An ability to understand the fundamental questions relevant to state of research in a selected sub-discipline as presented in CSC 7000 level courses.
An ability to advance the field in the core subject areas through the production of new software, algorithms, and models as documented in peer reviewed publications in the field.
The ability to pursue lifelong learning activities, which will allow them to successfully adapt to evolving technologies throughout their professional careers.

AD concentration highlights the follows:

Autonomous Driving elective courses. Students have to take no less than three courses to fulfill the depth requirement. Note: The following two focuses (real-time, artificial intelligence) are used only as a guideline for the students; students are free to select courses across the two focuses.

Focus on the real-time aspect of Autonomous Driving:

CSC 5280 Introduction to Cyber-Physical Systems
CSC 6280 Real-Time and Embedded Operating Systems
CSC 8260 Seminar in Networking, Distributed Systems and Parallel Systems

Focus on the real-time aspect of Autonomous Driving:

CSC 5825 Introduction to Machine Learning and Applications
CSC 6800 Artificial Intelligence I
CSC 6860 Digital Image Processing and Analysis
CSC 7800 Artificial Intelligence II
CSC 7860 Computer Vision

Rationale for Proposed Revisions:

Depth and Breadth Requirements:

The strong demand for high-level Autonomous Driving talent from market. Growth in the autonomous vehicle sector has been massive between 2015 and 2019: During this period, job postings increased by 833%, and job searches increased by 450%. At the same time, it looks like not enough people are taking rigorous autonomous driving related courses for this line of work.
Although there are several master programs in autonomous systems already, most of them are multi-disciplinary with an emphasis on autonomous robotics using traditional electronic and mechanical techniques. Consequently, a few of them give students a comprehensive understanding of state-of-the-art engineering practices used in the autonomous vehicle industry and let students get to interact with real data sets from a practical autonomous driving system.
Addition of the depth requirement to the breath requirement are expected to encourage computer science graduate students to focus more on autonomous driving related courses, honing their autonomous driving related technical skills and enhancing their competition in the job market.
Addition of the depth requirement to the breath requirement can be used as a template for creating other CS concentrations to better fit in the need from market.

AI Algorithms and Systems Major highlights the follows:

The proposed program requires 30 credits for graduation, either Plan A (24 credits of coursework plus 6 credits of master’s thesis) or Plan C (30 credits of coursework). All courses must be graduate-level courses offered within the College of Engineering. All course work must be completed in accordance with the regulations of the Graduate School and the College of Engineering.
A minimum grade point average of 3.00 for the MSAI program is required to obtain the master's degree. A maximum of one course in which a C has been received may be used to meet graduation requirements, provided this is offset by sufficient A grades to maintain the required 3.00 average.

Degree Requirements

9 credit hours from AI Algorithms and Systems core
3 credit hours from AI Hardware and Systems core
3 credit hours from Industrial AI core
Plan A: 9 credit hours from AI Program electives, including at least 6 credit hours from AI Hardware and Systems electives, or
Plan C: 15 credit hours from AI Program electives, including at least 12 credit hours from AI Hardware and Systems electives
Plan A: 6 credit hours of ECE 8999 master’s thesis

Core courses:

CSC 5825 5825 Introduction to Machine Learning and Applications
CSC 6800 Artificial Intelligence I
CSC 7760 Deep Learning

Elective courses:

CSC 5100 Introduction to Mobility
CSC 5272 Principles of Cyber Security
CSC 5280 Introduction to Cyber-Physical Systems
CSC 5430 Game Programming and Design I
& CSC 5431 Game Programming and Design I: Lab
CSC 5710 Design of Intelligent Information Retrieval Systems
CSC 5800 Intelligent Systems: Algorithms and Tools
CSC 5870 Computer Graphics I
CSC 5991 Special Topics in Computer Science
CSC 6430 Game Programming and Design II
& CSC 6431 and Game Programming and Design II: Lab
CSC 6710 Database Management Systems I
CSC 6860 Digital Image Processing and Analysis
CSC 7710 Database Management Systems II
CSC 7800 Artificial Intelligence II
CSC 7810 Data Mining: Algorithms and Applications
CSC 7825 Machine Learning
CSC 7991 Advanced Topics in Computer Science (Not repeatable)

Education

Overview

Artificial Intelligence (AI)

Autonomous Driving (AD)

AI Algorithms and Systems

AI concentration highlights the follows:

AI elective courses (take no less than four lecture courses to fulfill the depth requirement)

Rationale for Proposed Revisions:

Depth and Breadth Requirements:

Accommodations for Current CS M.S. Students:

Current M.S. Program Mission Statement:

Current M.S. Program Learning Objectives:

AD concentration highlights the follows:

Focus on the real-time aspect of Autonomous Driving:

Focus on the real-time aspect of Autonomous Driving:

Rationale for Proposed Revisions:

Depth and Breadth Requirements:

AI Algorithms and Systems Major highlights the follows:

Degree Requirements

Core courses:

Elective courses:

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