Program
M.Tech Artificial Intelligence & Machine Learning
APPLY NOWArtificial intelligence (AI) is a branch of computer science focused on making computers behave in human ways, such as rational thinking, perception and action. Machine Learning (ML) is a branch of artificial intelligence and one of the ways AI can be achieved. While AI initiatives make it possible for a machine to mimic human behavior based on what it has been taught, ML models help machines to teach themselves. This engineering program leads to a specialization in Artificial Intelligence and Machine Learning by combining important domains such as Mathematics, Computer Programming, Machine Learning, Statistics, and Software Design in order to mimic the complexity of human thought processes.
![b.tech](https://presidencyuniversity.in/uploads/course/64f9a06967a781694081129.webp)
-
Duration
2 Years Full-Time Program
Eligibility
Eligibility Norms For Admission To M.Tech Programs The candidate seeking admission for M. TECH program should have bachelor's degree or equivalent in Engineering [B.E/B. Tech] in the relevant field from any Indian university or Foreign University recognized by UGC/AIU, having obtained at least 50% of marks on the aggregate. 45% aggregate marks required in case of candidates belonging to SC/ST and other Reserved Categories. The candidate should also submit score details of any State/Central Entrance Examination/Presidency University admission qualifying examination for ADMISSION TOME/M. TECH program. Kindly note that taking one of the above tests is a pre-requisite for admission to the M. TECH Program.
Career
![img](https://presidencyuniversity.in/assets/images/aime-career.webp)
- Machine learning engineer
- AI Researcher
- Data Scientist
Curriculum
Sl. No. | Category of Courses | Descriptions of the Category |
---|---|---|
1 | SCHOOL CORE | All students of particular school have to compulsorily complete all the courses in this category |
2 | PROGRAM CORE | All students of particular programme have to compulsorily complete all the courses in this category |
3 | DISCIPLINE ELECTIVE | The students of the particular programme have a choice to pick up a set of courses from specialization baskets |
4 | OPEN ELECTIVE | The students have choice to pick up the courses from across the University offering (across all schools) courses |
Details For Courses
Sl. No. | Course Name |
---|---|
1 | Advanced Engineering Mathematics |
2 | English for Employability |
3 | Seminar - I |
4 | Seminar - II |
5 | Disseration/ Internship - I |
6 | Disseration/ Internship - II |
Sl. No. | Course Name |
---|---|
1 | Artificial Intelligence |
2 | Knowledge Engineering and Expert Systems |
3 | Machine Learning Algorithms |
4 | Deep Leaning |
5 | Natural Language Processing Techniques |
Sl. No. | Course Name |
---|---|
1 | Data Analytics and Visualization |
2 | Robotic Process Automation |
3 | Machine Vision |
4 | AI in Cloud Computing |
5 | Soft Computing Techniques |
6 | Ontology Engineering for the Semantic Web |
7 | Big Data Analyics Tools And Techniques |
8 | Time Series Analysis and Forecasting |
9 | Intelligent Information Retrival |
10 | AI in Internet of Things |
11 | Essentials for Machine Learning |
12 | Application of Probability theory in Computer Science |
13 | NoSQL Databases |
14 | Recommender Systems with Machine Learning and AI |