Comprehensive Exam Track: Total Credit Hours Required to Finish the Degree ( 36 Credit Hours ) as Follows
Specialization Requirements
Students must pass all of the following courses
|
Course Number |
Course Name |
Weekly Hours |
Cr. Hrs. |
Prerequisite |
||
|---|---|---|---|---|---|---|
|
Theoretical |
Practical |
|||||
| 152616000 | INTRODUCTION TO DATA SCIENCE | This course is an introduction to the emerging field of data science, a field that combines business strategy, information technology and modeling methods. Topics covered include the evolution of data science, the benefits and opportunities of data science and its relation to machine learning and data infrastructure uses. Special emphasis will be placed on practical techniques for analyzing data including the use of statistical modeling as a data tool. | 3 | - | 3 |
- |
| 152616010 | FOUNDATION OF AI | This course provides an overview of the field of artificial intelligence and its core techniques and applications. Topics covered include logic, constraint satisfaction, search, game playing, Markov decision processes and reasoning, planning and learning with certainty and uncertainty. Special emphasis will be placed on machine learning and its applications to real-world challenges. | 3 | - | 3 |
- |
| 152616020 | ADVANCED STATISTICS | 3 | - | 3 |
- |
|
| 152616030 | PROGRAMMING LANGUAGES | This course is an introduction to the systems and theory of the programming languages used in Artificial Intelligence and Data Science. Topics covered include evaluating the benefits and uses of and comparing and contrasting programming languages available to students. Special emphasis will be placed on Python and R and libraries such as TensorFlow and Scikit-learn will be introduced. This course will be updated with new languages as the field develops. | 3 | - | 3 |
- |
| 152616040 | RESEARCH METHODS | The course examines the role of business research and provides an overview of commonly used qualitative and quantitative research methods. Topics covered include scientific inquiry, the research process, proposal development, research design, hypothesis testing, primary and secondary data collection, statistical data analysis and presentation of research reports. | 3 | - | 3 |
- |
| 152616050 | MACHINE LEARNING | 3 | - | 3 |
152616010 FOUNDATION OF AI This course provides an overview of the field of artificial intelligence and its core techniques and applications. Topics covered include logic, constraint satisfaction, search, game playing, Markov decision processes and reasoning, planning and learning with certainty and uncertainty. Special emphasis will be placed on machine learning and its applications to real-world challenges. |
|
| 152616060 | NATURAL LANGUAGE PROCESSING | 3 | - | 3 |
152616010 FOUNDATION OF AI This course provides an overview of the field of artificial intelligence and its core techniques and applications. Topics covered include logic, constraint satisfaction, search, game playing, Markov decision processes and reasoning, planning and learning with certainty and uncertainty. Special emphasis will be placed on machine learning and its applications to real-world challenges. |
|
| 152616070 | DESIGN OF ALGORITHMS | 3 | - | 3 |
152616000 INTRODUCTION TO DATA SCIENCE This course is an introduction to the emerging field of data science, a field that combines business strategy, information technology and modeling methods. Topics covered include the evolution of data science, the benefits and opportunities of data science and its relation to machine learning and data infrastructure uses. Special emphasis will be placed on practical techniques for analyzing data including the use of statistical modeling as a data tool. |
|
| 152616080 | HUMAN-COMPUTER INTERACTION | 3 | - | 3 |
152616010 FOUNDATION OF AI This course provides an overview of the field of artificial intelligence and its core techniques and applications. Topics covered include logic, constraint satisfaction, search, game playing, Markov decision processes and reasoning, planning and learning with certainty and uncertainty. Special emphasis will be placed on machine learning and its applications to real-world challenges. |
|
| 152616120 | ROBOTICS | 3 | - | 3 |
152616010 FOUNDATION OF AI This course provides an overview of the field of artificial intelligence and its core techniques and applications. Topics covered include logic, constraint satisfaction, search, game playing, Markov decision processes and reasoning, planning and learning with certainty and uncertainty. Special emphasis will be placed on machine learning and its applications to real-world challenges. 152616070 DESIGN OF ALGORITHMS |
|
Students must pass ( 6 ) credit hours from any of the following courses
|
Course Number |
Course Name |
Weekly Hours |
Cr. Hrs. |
Prerequisite |
||
|---|---|---|---|---|---|---|
|
Theoretical |
Practical |
|||||
| 152616090 | COGNITION | 3 | - | 3 |
- |
|
| 152616100 | DATA MINING | 3 | - | 3 |
152616000 INTRODUCTION TO DATA SCIENCE This course is an introduction to the emerging field of data science, a field that combines business strategy, information technology and modeling methods. Topics covered include the evolution of data science, the benefits and opportunities of data science and its relation to machine learning and data infrastructure uses. Special emphasis will be placed on practical techniques for analyzing data including the use of statistical modeling as a data tool. |
|
| 152616110 | COMPUTER VISION | 3 | - | 3 |
- |
|
| 152616130 | SEMINAR OF ARTIFICIAL INTELLIGENCE | 3 | - | 3 |
152616010 FOUNDATION OF AI This course provides an overview of the field of artificial intelligence and its core techniques and applications. Topics covered include logic, constraint satisfaction, search, game playing, Markov decision processes and reasoning, planning and learning with certainty and uncertainty. Special emphasis will be placed on machine learning and its applications to real-world challenges. |
|
| 152616140 | DEEP LEARNING AND NEURAL NETWORKS | 3 | - | 3 |
152616050 MACHINE LEARNING |
|
| 152616150 | BUSINESS ANALYTICS | 3 | - | 3 |
152616020 ADVANCED STATISTICS |
|
Thesis\Treatise Track: Total Credit Hours Required to Finish the Degree ( 36 Credit Hours ) as Follows
Specialization Requirements
Students must pass all of the following courses plus ( 6 ) credit hours for the Thesis
|
Course Number |
Course Name |
Weekly Hours |
Cr. Hrs. |
Prerequisite |
||
|---|---|---|---|---|---|---|
|
Theoretical |
Practical |
|||||
| 152616000 | INTRODUCTION TO DATA SCIENCE | This course is an introduction to the emerging field of data science, a field that combines business strategy, information technology and modeling methods. Topics covered include the evolution of data science, the benefits and opportunities of data science and its relation to machine learning and data infrastructure uses. Special emphasis will be placed on practical techniques for analyzing data including the use of statistical modeling as a data tool. | 3 | - | 3 |
- |
| 152616010 | FOUNDATION OF AI | This course provides an overview of the field of artificial intelligence and its core techniques and applications. Topics covered include logic, constraint satisfaction, search, game playing, Markov decision processes and reasoning, planning and learning with certainty and uncertainty. Special emphasis will be placed on machine learning and its applications to real-world challenges. | 3 | - | 3 |
- |
| 152616020 | ADVANCED STATISTICS | 3 | - | 3 |
- |
|
| 152616030 | PROGRAMMING LANGUAGES | This course is an introduction to the systems and theory of the programming languages used in Artificial Intelligence and Data Science. Topics covered include evaluating the benefits and uses of and comparing and contrasting programming languages available to students. Special emphasis will be placed on Python and R and libraries such as TensorFlow and Scikit-learn will be introduced. This course will be updated with new languages as the field develops. | 3 | - | 3 |
- |
| 152616040 | RESEARCH METHODS | The course examines the role of business research and provides an overview of commonly used qualitative and quantitative research methods. Topics covered include scientific inquiry, the research process, proposal development, research design, hypothesis testing, primary and secondary data collection, statistical data analysis and presentation of research reports. | 3 | - | 3 |
- |
| 152616050 | MACHINE LEARNING | 3 | - | 3 |
152616010 FOUNDATION OF AI This course provides an overview of the field of artificial intelligence and its core techniques and applications. Topics covered include logic, constraint satisfaction, search, game playing, Markov decision processes and reasoning, planning and learning with certainty and uncertainty. Special emphasis will be placed on machine learning and its applications to real-world challenges. |
|
| 152616060 | NATURAL LANGUAGE PROCESSING | 3 | - | 3 |
152616010 FOUNDATION OF AI This course provides an overview of the field of artificial intelligence and its core techniques and applications. Topics covered include logic, constraint satisfaction, search, game playing, Markov decision processes and reasoning, planning and learning with certainty and uncertainty. Special emphasis will be placed on machine learning and its applications to real-world challenges. |
|
| 152616070 | DESIGN OF ALGORITHMS | 3 | - | 3 |
152616000 INTRODUCTION TO DATA SCIENCE This course is an introduction to the emerging field of data science, a field that combines business strategy, information technology and modeling methods. Topics covered include the evolution of data science, the benefits and opportunities of data science and its relation to machine learning and data infrastructure uses. Special emphasis will be placed on practical techniques for analyzing data including the use of statistical modeling as a data tool. |
|
Students must pass ( 6 ) credit hours from any of the following courses
|
Course Number |
Course Name |
Weekly Hours |
Cr. Hrs. |
Prerequisite |
||
|---|---|---|---|---|---|---|
|
Theoretical |
Practical |
|||||
| 152616080 | HUMAN-COMPUTER INTERACTION | 3 | - | 3 |
152616010 FOUNDATION OF AI This course provides an overview of the field of artificial intelligence and its core techniques and applications. Topics covered include logic, constraint satisfaction, search, game playing, Markov decision processes and reasoning, planning and learning with certainty and uncertainty. Special emphasis will be placed on machine learning and its applications to real-world challenges. |
|
| 152616090 | COGNITION | 3 | - | 3 |
- |
|
| 152616100 | DATA MINING | 3 | - | 3 |
152616000 INTRODUCTION TO DATA SCIENCE This course is an introduction to the emerging field of data science, a field that combines business strategy, information technology and modeling methods. Topics covered include the evolution of data science, the benefits and opportunities of data science and its relation to machine learning and data infrastructure uses. Special emphasis will be placed on practical techniques for analyzing data including the use of statistical modeling as a data tool. |
|
| 152616110 | COMPUTER VISION | 3 | - | 3 |
- |
|
| 152616120 | ROBOTICS | 3 | - | 3 |
152616010 FOUNDATION OF AI This course provides an overview of the field of artificial intelligence and its core techniques and applications. Topics covered include logic, constraint satisfaction, search, game playing, Markov decision processes and reasoning, planning and learning with certainty and uncertainty. Special emphasis will be placed on machine learning and its applications to real-world challenges. 152616070 DESIGN OF ALGORITHMS |
|
| 152616130 | SEMINAR OF ARTIFICIAL INTELLIGENCE | 3 | - | 3 |
152616010 FOUNDATION OF AI This course provides an overview of the field of artificial intelligence and its core techniques and applications. Topics covered include logic, constraint satisfaction, search, game playing, Markov decision processes and reasoning, planning and learning with certainty and uncertainty. Special emphasis will be placed on machine learning and its applications to real-world challenges. |
|
| 152616140 | DEEP LEARNING AND NEURAL NETWORKS | 3 | - | 3 |
152616050 MACHINE LEARNING |
|
| 152616150 | BUSINESS ANALYTICS | 3 | - | 3 |
152616020 ADVANCED STATISTICS |
|
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