COURSE OVERVIEW
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.
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.
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:
Programme Curriculum
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Semester - I +
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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
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Semester - II +
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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
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Semester - III +
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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
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Semester - IV +
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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
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Semester - V +
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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
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Semester - VI +
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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
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Semester - VII +
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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
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Semester - VIII +
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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
| 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 | Students must have completed their three-year engineering diploma in ANY branch of Engineering and Technology from a recognized institute to apply for this program. They also must have secured at least 50% marks in their diploma program. |
EMPLOYABILITY AREAS
IT graduates can explore employment opportunities in various public and private sectors. They mostly acquire the following positions:
- Hospital and Medicine
- Game Playing
- Speech Recognition
- Understanding
- Natural Language
- Computer Vision
- Cyber Security
- Face Recognition
- Transport
- Marketing & Advertising
- Language Detection Machine
- Robotics
FAQ
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Which is better: B.Tech CSE or B.Tech AI and ML? +
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Both degrees offer strong foundations in computer science. B.Tech in AI & ML provides specialised skills in intelligent systems and is ideal if you’re passionate about robotics, NLP and deep learning. In contrast, CSE offers broader career flexibility across software development, systems and infrastructure.
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Is a B.Tech in AI and ML good for the future? +
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Yes. AI/ML is expected to add $15.7 trillion to the global economy by 2030, with enormous demand in healthcare, finance and more. Salary prospects start at ₹6–15 LPA and can reach ₹25–50 LPA as experience grows.
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Which is better: CSE or AI and ML? +
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If you enjoy a versatile approach and wide industry options, CSE is ideal. If you’re certain about specialising in intelligent technologies, like neural networks, robotics, or NLP, then AI & ML is a better fit. Both are future-focused; the choice depends on your passion.
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What are the eligibility criteria for admission into B.Tech (CSE – AI and ML)? +
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Applicants must have:
- Passed 10+2 from a recognised board with ≥50% marks
- Studied Physics and Mathematics (compulsory)
- Admission is via an entrance exam or merit-based selection
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What career opportunities are available after completing B.Tech AI and ML? +
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Graduates can pursue roles such as:
- Machine Learning Engineer
- Data Scientist
- AI Research Scientist
- Computer Vision or NLP Engineer
- MLOps Engineer
- AI Product Manager
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What are the key topics covered in B.Tech (CSE – AI and ML)? +
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Core study areas include:
- Programming Fundamentals (Python, C/C++)
- Data Structures & Algorithms
- Probability & Statistics
- Machine Learning, Deep Learning, NLP, Computer Vision
- AI ethics, robotics, reinforcement learning
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What is the duration of the B.Tech (CSE – AI and ML) programme? +
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The programme spans 4 years, divided into eight semesters and typically includes industry-aligned projects and hands-on training.
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What is the cost of the AI and ML course? +
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At The Apollo University, the total fee for B.Tech CSE, AI & ML is ₹10,59,000 (₹2.63 lakhs per year including ₹13,000 admission fee). Other private universities may range ₹6–10 LPA/year, while public colleges can cost ₹2–5 LPA/year .
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What qualification is required for the B.Tech AI and ML course? +
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Candidates must have completed 10+2 with Physics and Mathematics and preferably Chemistry/Computer Science, securing at least 50% aggregate. Entrance exams or merit-based applications are used for admission.
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Is there any scholarship available for the B.Tech AI and ML course? +
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The Apollo University offers merit-based scholarships and financial aid, including scholarships for high achievers and economically disadvantaged students. Additionally, some state- or national-level scholarships might apply .
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What is the subject of AI and ML? +
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Key subjects include:
- Introduction to AI & ML
- Deep Learning / Neural Networks
- NLP & Speech Processing
- Computer Vision
- Reinforcement Learning, Robotics
- Ethics in AI, Data Analysis & Statistical Inference