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Master of Science in Artificial Intelligence is an extended degree program that is designed to provide students with knowledge in the creation of intelligent systems and programs. Some of the fields involve are machine learning, deep learning, neural networks, computer vision, natural language processing, robotics. The course structure of the M.Sc Artificial Intelligence program may take two years where it is broken down into four semesters.
The entrance exams that are considered for admission to M.Sc Artificial Intelligence program are – GATE Score, GRE, university conducted entrance tests, marks in bachelor degree. There are also few colleges which offer direct admission on the basis of marks scored in the degree course in computer science, electronics, mathematics, statistics. if it meets a certain percentage standard. In addition to the above requirements, passing of other exams like CAT, GMAT, CMAT also plays an important role in the admission into the Artificial Intelligence master’s program offered by different institutes.
| Highlight | Detail |
|---|---|
| Full Form | Master of Science in Artificial Intelligence |
| Duration | 2 years |
| Course Level | Postgraduate |
| Eligibility | Bachelor's degree in Computer Science, Mathematics, Engineering, or related fields, with a minimum required percentage (usually 50-60%) |
| Top Colleges | Indian Institutes of Technology (IITs), International Institute of Information Technology (IIIT) Hyderabad, University of Hyderabad, BITS Pilani, Vellore Institute of Technology (VIT) |
1. Candidate must be B.E/B.Tech holder from any stream with minimum 50% marks in computer science engineering.
2. Qualitative score in entrance examinations on national or state level like GATE/NET or examination taken by the institute.
3. Good understanding of certain topics such as data structures, algorithms, calculus, linear algebra and probability, and programming.
4. Sound reasoning ability, numerical skills and a flair for designing smart systems.
5. All courses are delivered in English for lecture notes and research papers comprehension.
6. They may demand work experience, experience in publishing, performance in interviews if they offer limited number of seats.
| Subject | Important Topics |
|---|---|
| Foundations of Artificial Intelligence | History of AI, Intelligent agents, Problem-solving techniques, Knowledge representation and reasoning |
| Machine Learning | Supervised learning, Unsupervised learning, Reinforcement learning, Deep learning, Neural networks |
| Natural Language Processing | Text preprocessing, Named entity recognition, Sentiment analysis, Machine translation, Chatbots |
| Computer Vision | Image processing, Feature extraction, Object detection and recognition, Image segmentation, CNNs |
| Robotics and Autonomous Systems | Robot kinematics and dynamics, Motion planning, Robot perception, Control systems, Autonomous vehicles |
| Data Mining and Big Data Analytics | Association rule mining, Clustering algorithms, Classification techniques, Big data platforms |
| Ethics and AI Governance | AI ethics frameworks, Bias and fairness in AI, Privacy concerns, Regulation and policy in AI |
| AI Applications | Healthcare applications, Finance and banking, Smart cities, Autonomous systems, AI in education |
| Research Methodology in AI | Literature review, Research design, Data analysis methods, Scientific writing in artificial intelligence |
| Specialization | Average Salary (INR) |
|---|---|
| Machine Learning | 5,00,000 - 12,00,000 |
| Computer Vision | 4,50,000 - 11,00,000 |
| Natural Language Processing (NLP) | 5,00,000 - 10,0,000 |
| Robotics | 5,00,000 - 14,00,000 |
| Deep Learning | 5,50,000 - 13,00,000 |
| Reinforcement Learning | 6,00,000 - 15,00,000 |
