M.Tech. (AI & ML) is a two-year degree program in Artificial Intelligence and Machine Learning at Indian Institute of Information Technology Sri City, Chittoor offered by the faculty of Computer Science and Engineering.

Artificial Intelligence has emerged with its potential ability to solve complex societal problems of recent times including education, healthcare, security, information forensics, visual understanding, efficient transportation, increased efficiency in providing e-governance services to the public, etc. The Govt. of India has initiated widespread discussion on the role of AI in India.

Being an Institute of National Importance in Information Technology, IIIT Sri City is already running the B.Tech. program with AI & ML specialization. Thus, the M.Tech. in Artificial Intelligence and Machine Learning program will boost the IIITS ecosystem further and will produce the highly skilled manpower to the industry. The two-year M.Tech. in Artificial Intelligence aims to bridge the urgent needs of the industry and to produce the high-end AI scientists and engineers for the society. The aim of the M.Tech. (AI) program is to produce graduates by providing rigorous training in the priority areas of Artificial Intelligence. The MTech in AI strengthens the students such that they can become AI world leaders and shape India's future in a better way. This is achieved through a curriculum focusing on Outcome Based Education (OBE), which follows a student-centric teaching and learning methodology designed to help students achieve well-defined objectives after completing the courses.

Teaching Methodology at IIIT Sri City

At IIIT Sri City, we broadly follow two teaching methodology simultaneously:

Classroom discussions conducted and facilitated by highly talented faculty members followed by Lab and tutorial sessions. Tutorial sessions are especially very helpful to those students who need extra help to excel in courses.

Research based projects to offer hands-on experience. Generally faculty members throw challenging technology related problems to students to work and come up with implementable solutions over the period of 2-4 semesters

The OBE Curriculum for M.Tech. (AIML)

At IIIT Sri City, we follow an Outcome Based Education (OBE) where the course delivery and assessment are carefully planned to achieve stated objectives and outcomes. 

Program Outcomes 

Program outcomes are specific focused statements that describe what students are expected to be able to do at the end of their graduation. These outcomes are expected to align closely with various attributes, a graduate is expected to demonstrate at the end of the programme.

The following Programme Outcomes are derived for the M.Tech. in AIML Programme offered by IIIT Sri City:

SNo PO ID Program Outcomes (POs) - CSE Programme
1 PO1 Ability to identify problems /opportunities where AIML can be applied and to identify the right AIML algorithms in such contexts
2 PO2 Ability to demonstrate critical thinking for solving challenging AIML problems
3 PO3 Ability to perform data engineering, developing and testing the AIML solutions for diverse applications
4 PO4 Ability to solve a given challenge through design and analysis of AIML algorithms and implement the same by means of an efficient and effective computer program
5 PO5 Ability to continuously learn theories, concepts, tools and adapt to the evolving AIML industry and research environment
6 PO6 Ability to work in diverse teams and contribute towards attainment of overall outcome/impact of the tasks/projects 
7 PO7 Ability to practice ethics, values and socially responsible behaviour in all possible situations
8 PO8 Ability to communicate clearly and precisely with individuals and groups for achieving timely and quality outcomes

There are 8 POs and out of which the first 5 POs are specific to building and enhancing the technical expertise of the students during the course of the specific programme and the last 3 POs are general POs that are essential to follow good practices adhering social, cultural and ethical values for the rest of their lives.

Credit Requirements

It is proposed that a student must successfully complete 64 credits for graduation of Master of Technology (M.Tech.) in AI & ML. The courses across 64 credits are proposed to be split as follows:

Category Credits Remarks
Program Core (20 + 8) 28 Core courses necessary for the foundations in AI & ML
Program Electives (12) 12 Suggested Elective Courses in AI & ML
Project Work (12 + 12) 24 Students will be encouraged to be in the industry for the project work
Total 64

M.Tech. in AI & ML Curriculum

The following is the curriculum for the students to be admitted to the Master of Technology (M.Tech.) programme in AI & ML degree .

Semester: 1

Type Code Course Name L-T-P-C
Core CAK101 Artificial Intelligence and Knowledge Representation 2-1-2-5
Core CML102 Machine Learning 2-1-2-5
Core CDS103 Advanced Data Structures and Algorithms 2-1-2-5
Core CMF104 Mathematical Foundations 3-1-0-4
Core CAE105 AI and Ethics 1-0-0-1
Total Credits 20 Credits 

Semester: 2

Type Code Course Name L-T-P-C
Core CDL201 Deep Learning 3-0-1-4
Core CDV202 Data Analytics and Visualization 3-0-1-4
Elective Elective - 1 3-0-1-4
Elective Elective - 2 3-0-1-4
Elective Elective - 3 3-0-1-4
Total Credits 20 Credits


Semester: 3

Type Code Course Name L-T-P-C
Project MPW301 Project Work - 1 0-0-12-12
Total Credits 12 Credits

Semester: 4

Type Code Course Name L-T-P-C
Project MPW401 Project Work - 2 0-0-12-12
Total Credits 12 Credits

One Lecture (L) Hour = 1 credit

Two Tutorial (T) Hours = 1 credit

Three Practical (P) Hours = 1 credit

C = Total  Credits (L+T+P)

List of Elective Courses

The following is the list of electives to be offered in the semester - 2 of the M.Tech. in AI & ML Programme:

  • Computer Vision (ECV203) 3-0-1-4
  • Natural Language Processing (ENL204) 3-0-1-4
  • Information Retrieval (EIR205) 3-0-1-4
  • Data Mining (EDM206) 3-0-1-4
  • Robotics (ERO207) 3-0-1-4
  • Probabilistic Graphical Models (EPM208) 3-0-1-4
  • Big Data Analytics (EBD209) 3-0-1-4
  • Reinforcement Learning (ERL210) 3-0-1-4
  • Advanced Optimization (EAO211) 3-1-0-4