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COURSE OVERVIEW

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The theory and development of computer systems that can perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making and translation and interpretation, are formalised as AI. Machine learning is a method of data analysis that automates analytical model building. The adoption of artificial intelligence/ Machine Learning (AI/ML) is growing worldwide. Organisations worldwide are adopting AI/ML in their business transformation journey for agility, resilience, innovation and scalability. With this backdrop. The Apollo University offers a four-year undergraduate B.Tech. CSE (Artificial Intelligence and Machine Learning) for laying a strong foundation by using the principles and technologies that consist of many facets of Artificial Intelligence, including logic, knowledge representation, probabilistic models and Machine Learning. This programme is best suited for students seeking to build world-class expertise in Artificial Intelligence and Machine Learning and emerging technologies, which help to stand apart in the crowd and grow careers in the upcoming technological era.


SYLLABUS >
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Programme Objectives

  • The students will be able to acquire the ability to design intelligent solutions for various business problems in a variety of domains and business applications and also explore fields such as neural networks, natural language processing, robotics, deep learning, computer vision, reasoning and problem-solving.
  • The students will get acquainted with machine learning methods, tools and computer algorithms used to train machines to analyse, understand and find hidden patterns in data and make predictions.
  • The eventual goal is to utilise data for self-learning, eliminating the need to programme machines in an explicit manner.
  • Identify problems where artificial intelligence techniques are applicable.
  • Applying selected basic AI techniques; judging the applicability of more advanced techniques.

Industry leader speak

The term Artificial Intelligence and Machine Learning have created a lot of buzz over the past several years with all the talk on autonomous vehicles, data analysis on a massive scale and the world’s leading organisations relying on machine learning and deep learning algorithms to improve business intelligence and refine business strategy. It was also considered to be among the most advanced technologies and techniques used to deploy artificial neural networks. Those dealing in computer science and engineering industries will find it beneficial to implement Artificial Intelligence and Machine Learning as they will be in high demand with the evolution of these technologies.

speaker

Challa Vijay Kumar

Technical Manager

Galaxe Solutions India Pvt. Ltd.

PROGRAMME HIGHLIGHTS

Students will have different career options after graduating in AI & ML, such as:

The programme has an unmatched range & depth and covers the widest variety of skill & knowledge areas required to develop advanced AI solutions.
Aiming to become expert Machine Learning Engineers & AI Scientists.
The programme offers a set of core courses and elective courses, allowing students to gain expertise in Advanced Deep learning, Computational Learning Theory, Speech Processing, Natural Language Processing, etc.
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Programme Curriculum

  • 3 Week Induction Programme

    Course Code

    Course Name

    		 Periods

    per week

    		 Credits

    Hours per week
    L T P
    BTMT1701 Engineering Physics 3 1 0 4 4
    BTMT1702 Engineering Mathematics 3 1 0 4 4
    BTMT1801 Problem Solving and Programming with C 3 1 0 4 4
    TAUT1101 Communicative English (University Core- I) 3 0 0 3 3
    — University Elective I 3 0 0 3 3
    BTML1701 Engineering Physics Lab 0 0 3 1.5 3
    BTML1801 Problem Solving and Programming with C Lab 0 0 3 1.5 3
    — IT Work shop 0 0 0 0 1
    — Design Thinking 0 0 0 0 1
    — Soft Skills 0 0 0 0 1
    — Mentoring 0 0 0 0 1
    — Technical Seminar 0 0 0 0 1
    — Library 0 0 0 0 1
    — Physical Activity 0 0 0 0 2
    — Extra-curricular activities 0 0 0 0 2
    — Co-curricular activity 0 0 0 0 1
    — Self-Learning 0 0 0 0 1
    TOTAL 15 3 6 21 36

  • Course Code

    Course Name

    		 Periods

    per week

    		 Credits

    Hours per week
    L T P
    BTMT1703 Probability & Statistics 3 1 0 4 4
    BTMT1802 Basic Electrical and Electronics Engineering 3 1 0 4 4
    BTMT1301 Data Structures 3 1 0 4 4
    BTMT1302 Python Programming 3 1 0 4 4
    TAUT1102 Environmental Studies (University Core -II) 3 0 0 0 3
    — University Elective II 3 0 0 3 3
    BTML1802 Basic Electrical and Electronics Engineering Lab 0 0 2 1 2
    BTML1301 Data Structures Lab 0 0 2 1 2
    BTML1302 Python Programming Lab 0 0 2 1 2
    — Mentoring 0 0 0 0 1
    — Co-curricular activity 0 0 0 0 1
    — Self-Learning 0 0 0 0 1
    — Physical Activity 0 0 0 0 2
    — Extra-curricular activities 0 0 0 0 2
    — Library 0 0 0 0 1
    TOTAL 18 4 6 22 36

  • Course Code

    Course Name

    		 Periods

    per week

    		 Credits

    Hours per week
    L T P
    BTMT2701 Discrete Mathematics and Graph Theory 3 1 0 4 4
    BTMT2301 Design and Analysis of Algorithms 3 1 0 4 4
    BTMT2302 Object Oriented Programming through Java 3 0 0 3 3
    BTMT2801 Digital Logic Design 3 0 0 3 3
    TAUT2101 University Core – III (Health and Wellness) 3 0 0 3 3
    — University Elective -III 3 0 0 3 3
    BTMT2303 Constitution of India 3 0 0 0 3
    BTML2301 Java Programming Lab 0 0 2 1 2
    BTML2801 Digital Logic Design lab 0 0 2 1 2
    — Mentoring 0 0 0 0 1
    — Co-curricular activity 0 0 0 0 1
    — Self-Learning 0 0 0 0 1
    — Physical Activity 0 0 0 0 2
    — Extra-curricular activities 0 0 0 0 2
    — Soft Skills Training 0 0 0 0 1
    — Certification course 0 0 0 0 1
    TOTAL 22 2 4 22 36

  • Course Code

    Course Name

    		 Periods

    per week

    		 Credits

    Hours per week
    L T P
    BTMT2307 Computer Organization and Architecture 3 1 0 4 4
    BTMT2901 Management for Engineers 3 0 0 3 3
    BTMT2501 Machine Learning 3 0 0 3 3
    BTMT2304 Database Management Systems 3 1 0 4 4
    BTMT2305 Operating Systems 3 1 0 4 4
    BTMT2502 Artificial Intelligence 3 0 0 3 3
    BTMT2306 Universal Human Values 3 0 0 0 3
    BTML2302 Database Management Systems Lab 0 0 2 1 2
    BTML2501 Machine Learning Lab 0 0 2 1 2
    — Internship – I* will be evaluated in

    VIII- Sem

    		 – – – – –
    — Mentoring 0 0 0 0 1
    — Aptitude and Logical Reasoning 0 0 0 0 1
    — Library 0 0 0 0 1
    — Physical Activity 0 0 0 0 2
    — Extra-curricular activities 0 0 0 0 2
    — Technical Seminar 0 0 0 0 1
    TOTAL 21 3 4 23 36

  • Course Code

    Course Name

    		 Periods

    per week

    		 Credits

    Hours per week
    L T P
    BTMT3301 Computer Networks 3 0 0 3 3
    BTMT3302 Automata and Compiler Design 3 1 0 4 4
    BTMT3501 Data Analysis and Visualization 3 0 0 3 3
    — Faculty Elective – I 3 0 0 3 3
    — Programme Elective – I 3 0 0 3 3
    BTMM3501 MOOC – I 0 0 0 3 3
    BTMT3901 Entrepreneurship and Start up Management 3 0 0 0 3
    BTML3301 Computer Networks Lab 0 0 2 1 2
    BTML3501 Exploratory Data Analytics with R Lab 0 0 2 1 2
    — Mentoring 0 0 0 0 1
    — Library 0 0 0 0 1
    — Physical Activity 0 0 0 0 2
    — Extra-curricular activities 0 0 0 0 2
    — Campus Recruitment Training 0 0 0 0 4
    TOTAL 18 1 8 23 36

    Faculty Elective -I Program Elective -I
    Course Code Course Name Course Code Course Name
     

    SOTT3401a

    		 Object Oriented Analysis and Design BTMT3601a Software Engineering
    SOTT3401b Software Engineering BTMT3601b Statistical Thinking for Data Science
     

    SOTT3401c

    		 Artificial Intelligence BTMT3601c Advanced Java Programming
     

    SOTT3401d

    		 Computer Organisation and Architecture BTMT3601d Network Security
    SOTT3401e Linux Programming BTMT3601e AI and ML in Business Models
    SOTT3401f Advanced Data Structures
    SOTT3401g Data Warehousing and Mining
    SOTT3401h Machine Learning
     

    SOTT3401i

    		 Differential Equations and Vector Calculus
    SOTT3401j Numerical Methods for Engineers
     

    SOTT3401k

    		 Mathematical Foundations of Cyber Security
    SOTT3401l Information Retrieval Systems
     

    SOTT3401m

    		 Cryptography and Network Security
     

    SOTT3401n

    		 Principles of Programming Languages
    SOTT3401o Unix and Shell Programming
    SOTT3401p Introduction to Psychology
    SOTT3401q Industry 4.0
    SOTT3401r Computer Vision

  • Course Code

    Course Name

    		 Periods

    per week

    		 Credits

    Hours per week
    L T P
    BTMT3502 Natural Language Processing 3 0 0 3 3
    BTMT3503 Advanced Machine Learning 3 0 0 3 3
    — Faculty Elective – II 3 0 0 3 3
    — Programme Elective – II 3 0 0 3 3
    — Programme Elective – III 3 0 0 3 3
    BTMM3502 MOOC – II 0 0 0 3 3
    BTML3502 Natural Language Processing Lab 0 0 2 1 2
    BTML3503 Data Mining and Machine Learning Lab 0 0 2 1 2
    — Internship – II*  will be evaluated in VIII Sem – – – – –
    — Mentoring 0 0 0 0 1
    — Library 0 0 0 0 1
    — Physical Activity 0 0 0 0 2
    — Extra-curricular activities 0 0 0 0 2
    — Valued added courses 0 0 0 0 2
    — Seminar 0 0 0 0 1
    — Technical Training 0 0 0 0 3
    — Self-Learning 0 0 0 0 2
    TOTAL 15 0 4 20 36

     

    Faculty Elective -II Program Elective -II
    Course Code Course Name Course Code Course Name
    SOTT3402a Design Patterns BTMT3602a Image Processing
    SOTT3402b Cyber Security BTMT3602b Optimization Techniques in Machine Learning
    SOTT3402c Software Project Management BTMT3602c Deep Learning
    SOTT3402d Agile Software Development BTMT3602d Explainable AI
    SOTT3402e Cloud Computing BTMT3602e Soft Computing
    SOTT3402f Mobile Computing Program Elective -III
    SOTT3402g Image Processing BTMT3603a High Performance computing
    SOTT3402h Deep Learning BTMT3603b Generative AI
    SOTT3402i E-Commerce BTMT3603c Mern Technologies
     

    SOTT3402j

    		 Block Chain Technology BTMT3603d Network Programming
     

    SOTT3402k

    		 Mathematical Foundations of Data Science BTMT3603e Al in Gaming
     

    SOTT3402l

    		 Transforms and Boundary Value Problems
    SOTT3402m Optimization Techniques
    SOTT3402n Information Security
    SOTT3402o Ethical Hacking
    SOTT3402p Internet of Things
    SOTT3402q Embedded Systems
    SOTT3402r Green Computing
    SOTT3402s Cloud Security
    SOTT3402t DevNet
    SOTT3402u Advanced Computer Networks
    SOTT3402v Network Security
    SOTT3402w Fault Tolerant Systems
    SOTT3402x Computational Intelligence
    SOTT3402y Data Analytics with Tableau
    SOTT3402z Human Computer Interaction

  • Course Code

    Course Name

    		 Periods

    per week

    		 Credits

    Hours per week
    L T P
    BTMT4501 Big Data Analytics 3 0 0 3 3
    BTMT4502 Neural Networks and Deep Learning 3 0 0 3 3
    — Programme Elective – IV 3 0 0 3 3
    — Programme Elective – V 3 0 0 3 3
    BTMM4501 MOOC–III 0 0 0 3 3
    BTMP4301 Mini Project 0 0 2 1 2
    BTML4501 Big Data Analytics Lab 0 0 2 1 2
    BTML4502 Neural Networks and Deep Learning Lab 0 0 2 1 2
    — Mentoring 0 0 0 0 1
    — Library 0 0 0 0 1
    — Physical Activity 0 0 0 0 2
    — Extra-curricular activities 0 0 0 0 2
    — Valued added courses 0 0 0 0 3
    — Seminar 0 0 0 0 2
    — Technical Training 0 0 0 0 2
    — Technical Paper Writing 0 0 0 0 2
    TOTAL 12 0 10 20 36

     

    Program Elective -IV Program Elective -V
    Course Code Course Name Course Code Course Name
    BTMT4601a Reinforcement Learning BTMT4602a Pattern Recognition &   Visual Recognition
    BTMT4601b AI in Block Chain BTMT4602b Health Analytics
    BTMT4601c Autonomous Systems BTMT4602c Business Intelligence and Analytics
    BTMT4601d Social Media Analytics BTMT4602d Image and Video Analytics
    BTMT4601e Algorithms for DNA Sequencing BTMT4602e Machine Learning and Computer Security

  • Course Code

    Course Name

    		 Periods

    per week

    		 Credits

    Hours per week
    L T P
    BTMP4502 Project Work 0 0 24 12 24
    BTMI3501 Internship – I Evaluation# 0 0 0 2 0
    BTMI4501 Internship – II Evaluation# 0 0 0 2 0
    TOTAL 0 0 24 16\ 24

PROGRAMME FEE AND SCHOLARSHIPS

Fees Structure SCHOLARSHIPS ADMISSION PROCESS
One-Time Fee Admission Fee ₹ 7,000
1st Year Tuition Fee + Annual Recurring Fee ₹ 2,50,000 + ₹ 13,000
2nd Year Tuition Fee + Annual Recurring Fee ₹ 2,50,000 + ₹ 13,000
3rd Year Tuition Fee + Annual Recurring Fee ₹ 2,50,000 + ₹ 13,000
4th Year Tuition Fee + Annual Recurring Fee ₹ 2,50,000 + ₹ 13,000
Total Programme Fee (4 Years) ₹ 10,59,000

Scholarship is available for eligible students

Eligibility

Candidates must secure 50% in Physics, Chemistry and Mathematics of Intermediate or in the diploma course or must have appeared for Class 12 or equivalent examination with Physics, Chemistry and Mathematics as major subjects from any recognised board. Candidates who have completed or qualified for the final year of a diploma in engineering courses are also eligible. Candidates must not exceed the age of 24 years and the minimum age for applying is 17 years as of 31st December of the Calendar Year.

Lateral Entry

Total Course Duration You Will Join In Seats Available Education Qualification
3 Years 2nd Year 6 A three-year engineering diploma in Computer Science programme from a recognised institute. The candidates must have secured at least 50% marks in their diploma programme.

EMPLOYABILITY AREAS

IT graduates can explore employment opportunities in various public and private sectors. They mostly acquire the following positions:

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  • Hospital and Medicine
  • Game Playing
  • Speech Recognition
  • Understanding
  • Natural Language
  • Computer Vision
  • Cyber Security
  • Face Recognition
  • Transport
  • Marketing & Advertising
  • Language Detection Machine
  • Robotics