The Master of Science (MS) in Artificial Intelligence program offers a comprehensive exploration of artificial intelligence (AI) theories, methodologies, and applications. Through a combination of rigorous coursework and practical projects, students gain expertise in areas such as machine learning, deep learning, natural language processing, and computer vision. With a focus on both theoretical foundations and real-world problem-solving, graduates are equipped to spearhead innovations across industries, from developing advanced AI algorithms to creating intelligent systems that enhance decision-making and automation.
The career path for graduates with an MS in Artificial Intelligence is diverse and dynamic, offering opportunities in various industries and roles. Some common career paths include:
AI Researcher, Machine Learning Engineer, Data Scientist, AI Software Developer, AI Consultant, Natural Language Processing (NLP) Engineer, Computer Vision Engineer, AI Ethics and Policy Analyst, These associated career paths offer opportunities for growth, specialization, and impact, allowing graduates to contribute to the advancement of AI and make meaningful contributions to society and industry.
Entry Requirement for MS Software Engineering:
Semester Wise Duration of Degree:
Minimum: 3 semesters
Maximum: 8 Semesters
Years wise Duration of Degree:
Minimum: 1 ½ Years
Maximum: 4 Years
Minimum Credit Hours requirement for the degree: 32 CHs
| Code | Course Title | Credit Hours | Labs | Total |
1 | COMP-830 | Tools and Techniques for Data Science | 2 | 1 | 3 |
2 | COMP-842 | Advanced Machine Learning | 3 |
| 3 |
3 |
| Elective-I | 3 |
| 3 |
|
|
|
|
|
|
|
| · |
|
|
|
|
| Total Credit Hours | 9 |
| 9 |
| Code | Course Title | Credit Hours | Labs | Total |
1 | COMP-844 | Advanced Deep Learning | 3 |
| 3 |
2 | COMP-XXX | Elective II | 3 |
| 3 |
3 | COMP-XX | Elective III | 3 |
| 3 |
|
| Total Credit Hours | 9 |
| 9 |
| Code | Course Title | Credit Hours | Labs | Total |
1 | COMP-898 | MS Research work | 3 |
| 3 |
2 | COMP-XXX | Elective-IV | 3 |
| 3 |
3 |
| Total Credit Hours | 6 |
| 6 |
| Code | Course Title | Credit Hours | Labs | Total |
1 | COMP-XXX | Elective-V | 3 |
| 3 |
2 | COMP-899 | MS Thesis | 3 |
| 3 |
|
| Total Credit Hours | 6 |
| 6 |
|
| Specialized Core Courses: |
|
|
|
1 | COMP-844 | Advanced Machine Learning | 3 |
| 3 |
2 | COMP-845 | Advanced Deep learning | 3 |
| 3 |
3 | COMP-830 | Tools and Techniques for Data Science | 2 | 1 | 3 |
|
|
|
|
|
|
|
| Elective Courses |
|
|
|
1 | COMP-846 | Advanced Expert and Recommender Systems | 3 |
| 3 |
2 | COMP-940 | Computer Vision and Pattern Identification | 3 |
| 3 |
3 | COMP-832 | Advanced Information Retrieval | 3 |
| 3 |
4 | COMP-833 | Advanced Big Data Analytics | 3 |
| 3 |
5 | COMP-834 | Advanced Data Visualization | 3 |
| 3 |
6 | COMP-840 | Statistical and Mathematical Methods for Data Analysis | 3 |
| 3 |
7 | COMP-841 | Machine Learning for Data Science | 3 |
| 3 |
8 | COMP-843 | Advanced Artificial Neural Network | 3 |
| 3 |
9 | COMP-845 | Advanced Natural Language Processing | 3 |
| 3 |
10 | COMP-847 | Advanced Digital Image Processing | 3 |
| 3 |
11 | COMP-848 | Advanced Knowledge Representation and Reasoning | 3 |
| 3 |
12 | COMP-849 | Computational Intelligence and IoT | 3 |
| 3 |
13 | COMP-941 | AI for Biomedical Engineering | 3 |
| 3 |
14 | COMP-942 | AI in Smart Energy Systems | 3 |
| 3 |
15 | COMP-943 | Distributed Data Processing and Machine Learning | 3 |
| 3 |
16 | COMP-990 | Advanced Topics in Computing | 3 |
| 3 |
17 | COMP-831 | Knowledge Engineering | 3 |
| 3 |