You can apply online through the official website. Admission is based on academic merit or scores from recognized entrance exams.
About Programme
The BTech CSE - Specialization in AIML at Apex University provides students with a comprehensive understanding of the latest advances in artificial intelligence and machine learning technologies. Students will develop expertise in core AIML algorithms, such as deep learning and neural networks, computer vision, natural language processing and robotics. The course also covers using AI tools for data analysis and decision-making, as well as strategies for deployment and integration into business processes. This program provides an opportunity to gain hands-on experience in developing AI applications and systems, enabling students to acquire the necessary skills to become successful in AI. Furthermore, the program focuses on providing an ethical and mindful approach to developing AI systems so that students understand the implications of their decisions on society and the environment. With a solid foundation in AIML and hands-on experience, the BTech CSE (AIML) at Apex University is the ideal choice for those looking to enter the field of AI and machine learning.
Course Details
Eligibility Criteria
Passed 10+2 examination with Physics / Mathematics / Chemistry / Computer Science / Electronics / Information Technology / Biology / Informatics Practices / Biotechnology / Technical Vocational subject / Agriculture / Engineering Graphics / Business Studies / Entrepreneurship. (Any of the three) Obtained at least 50% marks for General (45% marks in case of candidates belonging to reserved category) in the above subjects taken together.
Fees Per Semester
65000/-
Duration / Schedule
4 Years (8 Semesters)
Seat Metrix
50% CUET and 50 % AUCET
SCHOLARSHIP AVAILABLE
Scholarship up-to 10 Lacs available for meritorious students
Check Eligibility
Programme Structure
| Semester 1 | |
|---|---|
| THEORY PAPER | PRACTICAL PAPER |
| - Engineering Mathematics-I | - Engineering Physics Lab |
| - Engineering Physics | - Basic Electronics and Electrical Engineering Lab |
| - Basic Electronics and Electrical Engineering | - Workshop Practice Lab |
| - English-I | - Basic Mechanical Engineering Lab |
| - Basic Mechanical Engineering | - English Language Lab |
| Semester 2 | |
|---|---|
| THEORY PAPER | PRACTICAL PAPER |
| - Engineering Mathematics-II | - Engineering Chemistry Lab |
| - Engineering Chemistry | - Programming with C for Problem Solving Lab |
| - Programming with C for Problem Solving | - Engineering Graphics and Design Lab |
| - Human Values | - Digital Electronics and Logic Design Lab |
| - Engineering Graphics and Design | |
| - Digital Electronics and Logic Design | |
| Semester 3 | |
|---|---|
| THEORY PAPER | PRACTICAL PAPER |
| - Discrete Mathematics | - Operating Systems Lab |
| - Computer Fundamentals and Organization | - Data Structures and Algorithms Lab |
| - Operating Systems | - Object Oriented Programming with Java Lab |
| - Data Structures and Algorithms | - Social Outreach, Discipline and Curriculum Activities |
| - Object Oriented Programming with Java | |
| - Critical Thinking | |
| Semester 4 | |
|---|---|
| THEORY PAPER | PRACTICAL PAPER |
| - Database Management Systems | - Basics of Accounting |
| - Probability and Statistics | - Database Management Systems Lab |
| - Python Programming | - Python Programming Lab |
| - Knowledge Representation and Reasoning | - Coding and Computational Thinking-I |
| - Communication Skills | |
| - Principles of Management | |
| Semester 5 | |
|---|---|
| THEORY PAPER | PRACTICAL PAPER |
| - Web Technology | - Web Technology Lab |
| - Signals and Digital Image Processing | - Signals and Digital Image Processing Lab |
| - Machine Learning | - Machine Learning Lab |
| - Collaborative Team Work | - Soft Skills and Aptitude-I |
| - Introduction to Entrepreneurship | - Coding and Computational Thinking -II |
| - Necessary Statistics | - Mini Project |
| Semester 6 | |
|---|---|
| THEORY PAPER | PRACTICAL PAPER |
| - Web Technology | - Web Technology Lab |
| - Signals and Digital Image Processing | - Signals and Digital Image Processing Lab |
| - Machine Learning | - Machine Learning Lab |
| - Collaborative Team Work | - Soft Skills and Aptitude-I |
| - Introduction to Entrepreneurship | - Coding and Computational Thinking -II |
| - Necessary Statistics | - Mini Project |
| Semester 7 | |
|---|---|
| THEORY PAPER | PRACTICAL PAPER |
| - Numerical Methods and Optimization Techniques | - Deep Learning Lab |
| - Deep Learning | - Recommender System Lab |
| - Recommender System | - Departmental Elective I Lab |
| - R Programming | - R Programming Lab |
| - Data Analytics using SQL | - Data Analytics using SQL Lab |
| - Open Elective-IV | - Project Based Internship |
| - Principles of Banking | |
| - Production and Operations Management | |
| Semester 8 | |
|---|---|
| THEORY PAPER | PRACTICAL PAPER |
| - Chatbot Development | - Chatbot Development Lab |
| - Computer Vision | - Computer Vision Lab |
| - Open Elective-V | - Major Project |
| - Customer Relationship Management | |
| - Financial Services | |
Programme Objectives
B.Tech in AIML Program at Apex University
The B.Tech in AIML program at Apex University is designed to cultivate the next generation of AI innovators and thought leaders. Through an immersive and hands-on curriculum, students gain the expertise needed to excel in the rapidly evolving field of AI and ML.
Program Objectives:
- Master Core Concepts: Provide a deep understanding of AI and ML algorithms, neural networks, and natural language processing.
- Practical Implementation: Offer extensive hands-on experience through lab work, internships, and real-world projects, enabling students to apply theoretical knowledge.
- Innovative Solutions: Encourage creativity and innovation in developing AI-driven applications and solutions that address contemporary challenges.
- Analytical Excellence: Foster strong analytical and problem-solving skills to tackle complex AI and ML problems.
- Ethical AI Practices: Integrate ethical considerations and responsible AI practices into the learning process, ensuring graduates build AI solutions that benefit society.
- Career Readiness: Prepare students for diverse roles in AI, ML, data science, and beyond, equipping them with the skills to thrive in various industries.
- Professional Growth: Develop essential professional skills, including teamwork, communication, and project management, for a successful career in AI and ML.
Course USPs
- Cutting-Edge Curriculum: Comprehensive coverage of AI and ML topics including neural networks, natural language processing, computer vision, and deep learning, ensuring students stay ahead in the field.
- Hands-On Learning: Emphasis on practical experience through projects, internships, and lab work, enabling students to apply AI and ML concepts to real-world problems.
- Research Opportunities: Access to advanced research centers and labs, encouraging students to engage in innovative AI and ML research projects.
- Industry Collaboration: Strong partnerships with tech companies for guest lectures, internships, and collaborative projects, providing industry exposure and networking opportunities.
- Expert Faculty: Guidance from experienced professors and industry practitioners with deep knowledge in AI and ML, offering mentorship and support.
- Interdisciplinary Approach: Integration of AI and ML with other fields such as data science, robotics, and cybersecurity, broadening students' skill sets and career prospects.
- Career Readiness: Preparation for diverse roles such as data scientists, machine learning engineers, AI researchers, and more, with a focus on developing critical thinking and problem-solving skills.
- Global Opportunities: Exposure to international collaborations, conferences, and exchange programs, enhancing students' global perspectives and career opportunities.
- State-of-the-Art Facilities: Access to modern labs, computing resources, and AI tools, providing an optimal learning environment for mastering AI and ML.
Career Opportunities
Robotics Engineer:
Designs, develops, and tests intelligent robotic systems with AI-driven automation capabilities, improving efficiency and reducing human intervention in various industries.
Data Scientist:
Extracts insights from structured and unstructured data using AI-driven methodologies, statistical models, and machine learning techniques to drive strategic business decisions.
Machine Learning Scientist:
Researches and develops machine learning algorithms, improves automation, and enhances predictive analytics for various industries, including healthcare, finance, and marketing.
AI Engineer:
Develops AI models, automates processes, enhances business decision-making using deep learning and neural networks, and integrates AI-driven solutions into real-world applications.
Frequently Asked Questions
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1. How can I apply for the program?
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2. Are there scholarships available for this program?Yes, scholarships are offered based on academic performance, entrance exam results, and financial need.
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3. How can I apply for a scholarship?You can apply for scholarships during the admission process. Ensure that you provide all required documents, including entrance test scores, academic records, and any other necessary certifications.
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4. Is there a management quota or direct admission available?Apex University primarily admits students through AUCET and CUET. However, for details about specific admission pathways, you may contact the admissions office.
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5. What facilities are available for students at Apex University?Apex University provides state-of-the-art labs, a fully equipped library, modern classrooms, sports facilities, and hostel facility.
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Is there a focus on AI ethics in this course?Yes, topics like AI ethics, data privacy, and algorithmic bias are part of the curriculum.
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What kind of internships can AIML students pursue?Students can intern with tech firms, AI startups, or research organizations focusing on machine learning.
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Are hands-on projects included in the AIML curriculum?Yes, students work on real-world projects like predictive analytics, chatbots, and recommendation systems.
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Which programming languages are taught for AI/ML?Python, R, and MATLAB are commonly taught for AI/ML development.
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What is the scope of AI and ML in the industry?AI and ML are highly in demand, with applications in healthcare, finance, robotics, and autonomous systems.
Placement Highlights
38LPA
Highest Package
10LPA
Avg. Package
14k+
Total Placements
700+
Placement Drives
Our Top Recruiters
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