.png&w=1920&q=75)
BE in Artificial Intelligence Machine Learning at Amruta Institute of Engineering and Management Sciences
Ramanagara, Karnataka
.png&w=1920&q=75)
About the Specialization
What is Artificial Intelligence & Machine Learning at Amruta Institute of Engineering and Management Sciences Ramanagara?
This Artificial Intelligence & Machine Learning program at Amruta Institute of Engineering and Management Sciences focuses on providing a robust foundation in AI/ML principles, algorithms, and applications. The curriculum, aligned with current industry demands in India, emphasizes practical skills and theoretical knowledge required for developing intelligent systems. Key differentiators include a strong emphasis on data science, deep learning, and ethical AI, preparing students for the rapidly evolving tech landscape.
Who Should Apply?
This program is ideal for fresh graduates with a strong aptitude for mathematics and programming seeking entry into high-growth tech careers. It also caters to working professionals aiming to upskill in cutting-edge AI/ML domains, and career changers transitioning into the vibrant Indian AI industry. Ideal candidates typically possess a background in science or engineering with a keen interest in problem-solving and innovation.
Why Choose This Course?
Graduates of this program can expect to pursue India-specific career paths as AI Engineers, Machine Learning Scientists, Data Analysts, and AI Consultants across various sectors. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals commanding INR 10-25+ LPA in leading Indian companies. The program aligns with professional certifications like those from NVIDIA or Google, fostering continuous growth trajectories in the dynamic AI market.
Student Success Practices
Foundation Stage
Master Core Programming & Math- (Semester 1-2)
Dedicate significant time to mastering C programming, Data Structures, and foundational mathematics (Calculus, Linear Algebra, Probability). These form the bedrock for advanced AI/ML concepts. Practice coding regularly, solve problems on platforms like HackerRank or LeetCode, and revisit mathematical concepts frequently.
Tools & Resources
GeeksforGeeks, CodeChef, Khan Academy, NPTEL lectures on foundational subjects
Career Connection
A strong foundation ensures efficient algorithm development, robust model implementation, and understanding of complex AI research, directly impacting success in technical interviews and project development.
Build a Strong Academic Network- (Semester 1-2)
Engage actively with professors, senior students, and peers. Form study groups to tackle challenging subjects like Discrete Mathematics and Analog & Digital Electronics. Participate in department workshops and seminars to gain early insights into emerging tech trends and potential research areas.
Tools & Resources
Department clubs, Study groups, Professor office hours
Career Connection
Networking facilitates peer learning, opens doors to mentorship, collaborative projects, and can lead to valuable recommendations for internships and job opportunities.
Explore Basic AI/ML Concepts- (Semester 1-2)
While the formal AIML curriculum starts in later semesters, begin exploring introductory AI/ML concepts through online courses. Understand fundamental terminologies and the breadth of applications to build early interest and context for future studies.
Tools & Resources
Coursera (Andrew Ng''''s ML course), Kaggle for beginner datasets, YouTube tutorials
Career Connection
Early exposure helps develop a competitive edge, informs elective choices, and provides a head start in understanding the core specialization, crucial for internships.
Intermediate Stage
Engage in Practical ML Projects- (Semester 3-5)
Beyond lab work, undertake personal projects applying Machine Learning and Deep Learning concepts using real-world datasets. Focus on building complete solutions, from data preprocessing to model deployment. Participate in hackathons and coding competitions to test skills under pressure.
Tools & Resources
Kaggle competitions, GitHub for project showcasing, TensorFlow/PyTorch frameworks
Career Connection
Hands-on projects are critical for building a strong portfolio, demonstrating practical skills to potential employers, and securing internships in specialized AI/ML roles.
Seek Industry Exposure & Mentorship- (Semester 3-5)
Actively look for short-term internships, summer training programs, or virtual mentorship opportunities with AI/ML professionals. Attend industry conferences and workshops held in Bangalore or other tech hubs. Focus on understanding the practical challenges and business applications of AI.
Tools & Resources
LinkedIn for networking, Internshala for internships, Local tech meetups
Career Connection
Industry exposure provides invaluable insights into corporate culture, current technologies, and helps in building professional networks, significantly enhancing placement prospects.
Deepen Specialization through Electives- (Semester 5)
Carefully choose professional electives in areas like Natural Language Processing, Computer Vision, or Reinforcement Learning based on career interests. Supplement classroom learning with advanced online courses and research papers in chosen specialized areas.
Tools & Resources
NPTEL advanced courses, arXiv for research papers, Udemy/edX for specialized topics
Career Connection
Specialized knowledge makes you a more attractive candidate for specific roles and provides a competitive advantage in a highly specialized job market.
Advanced Stage
Execute a Capstone AI/ML Project- (Semester 7-8)
Leverage the final year project (Phase I & II) to develop a significant AI/ML application addressing a real-world problem. Focus on robust design, implementation, evaluation, and documentation. Aim for publication in a conference or a functional prototype.
Tools & Resources
Jupyter notebooks, Cloud platforms (AWS/Azure/GCP), Version control (Git)
Career Connection
A strong capstone project is the centerpiece of your resume, showcasing advanced technical skills, problem-solving abilities, and readiness for industry roles, leading to better placements.
Prepare Rigorously for Placements- (Semester 7-8)
Intensively prepare for technical interviews covering data structures, algorithms, ML concepts, and system design. Practice mock interviews, solve case studies, and refine your resume and LinkedIn profile. Focus on soft skills like communication and problem articulation.
Tools & Resources
LeetCode, InterviewBit, Glassdoor, College placement cell workshops
Career Connection
Comprehensive preparation is essential for cracking technical interviews at top Indian and global companies, leading to successful career placements and higher starting salaries.
Engage in Technical Writing & Seminars- (Semester 7-8)
Develop strong technical communication skills by writing research papers, blogs, or presenting technical seminars. Focus on clearly articulating complex AI/ML concepts and project outcomes. Participate in technical seminar events and seek feedback.
Tools & Resources
LaTeX for papers, Medium/LinkedIn for blogs, Toastmasters (if available)
Career Connection
Effective communication is crucial for professional growth, presentations in corporate settings, and contributing to technical communities, enhancing visibility and leadership potential.
Program Structure and Curriculum
Eligibility:
- A candidate who has passed 2nd PUC / 12th Std / Equivalent examination with English as one of the languages and obtained a minimum of 45% of marks in aggregate in Physics and Mathematics as compulsory subjects along with Chemistry / Bio-Technology / Biology / Electronics / Computer Science / Technical Vocational subject. In case of SC / ST / Category-1 and other backward classes, the minimum marks for eligibility shall be 40% in aggregate in the optional subjects in the qualifying examination.
Duration: 8 semesters / 4 years
Credits: 152 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21MATS11 | Engineering Mathematics - I | Core | 4 | Differential Calculus, Integral Calculus, Vector Calculus, Partial Differential Equations, Laplace Transforms |
| 21CHET12 | Engineering Chemistry | Core | 3 | Electrochemistry, Corrosion and its Control, Material Chemistry, Fuel Chemistry, Environmental Chemistry |
| 21CHEL13 | Engineering Chemistry Laboratory | Lab | 1 | Volumetric Analysis, Instrumental Methods, Material Characterization, Water Quality Analysis |
| 21ELN14 | Basic Electrical and Electronics Engineering | Core | 3 | DC Circuits, AC Circuits, Semiconductor Devices, Digital Logic Basics, Transistors and Amplifiers |
| 21GPL15 | Programming for Problem Solving | Core | 3 | C Programming Fundamentals, Control Structures, Functions and Arrays, Pointers and Strings, Structures and File I/O |
| 21GPL16 | Programming for Problem Solving Laboratory | Lab | 1 | C Program Implementation, Debugging Techniques, Algorithm Tracing, Problem-solving using C |
| 21EGD17 | Engineering Graphics | Core | 2 | Orthographic Projections, Isometric Projections, Sections of Solids, Development of Surfaces, Introduction to CAD |
| 21IDT18 | Innovation and Design Thinking | Skill Enhancement | 1 | Design Thinking Process, Empathy Mapping, Ideation Techniques, Prototyping, Problem Solving Approaches |
| 21PECS19 | Physical Education & Sports / Yoga | Audit Course | 0 | Physical Fitness, Sports Rules, Yoga Asanas, Mental Well-being |
| 21CIP110 | Constitution of India and Professional Ethics / Vyavaharika Kannada | Audit Course | 0 | Indian Constitution, Fundamental Rights, Professional Ethics, Cyber Ethics |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21MATS21 | Engineering Mathematics - II | Core | 4 | Linear Algebra, Vector Spaces, Fourier Series, Numerical Methods, Probability Distributions |
| 21PHYT22 | Engineering Physics | Core | 3 | Quantum Mechanics, Lasers and Optics, Materials Science, Solid State Physics, Wave Mechanics |
| 21PHYL23 | Engineering Physics Laboratory | Lab | 1 | Optical Experiments, Electrical Measurements, Material Testing, Semiconductor Characterization |
| 21FME24 | Elements of Mechanical Engineering | Core | 3 | Thermodynamics Basics, Power Cycles, Fluid Mechanics, Machine Elements, Manufacturing Processes |
| 21CVE25 | Elements of Civil Engineering | Core | 3 | Building Materials, Structural Elements, Surveying Principles, Water Resources, Geotechnical Engineering |
| 21WSL26 | Workshop Practice | Lab | 1 | Fitting Operations, Welding Techniques, Carpentry Skills, Sheet Metal Work |
| 21EGH27 | Professional English / Technical Kannada | Skill Enhancement | 1 | Technical Communication, Report Writing, Presentation Skills, Grammar and Vocabulary |
| 21EVSC28 | Environmental Studies | Core | 1 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources, Waste Management, Sustainable Development |
| 21KAS29 | Vyavaharika Kannada / Constitution of India and Professional Ethics | Audit Course | 0 | Indian Constitution, Fundamental Rights, Professional Ethics, Cyber Ethics |
| 21PECS210 | Physical Education & Sports / Yoga | Audit Course | 0 | Physical Fitness, Sports Rules, Yoga Asanas, Mental Well-being |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS31 | Data Structures and Applications | Core | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting and Searching, Hashing Techniques |
| 21AIML32 | Introduction to Artificial Intelligence and Machine Learning | Core | 4 | Foundations of AI, Problem-Solving with AI, Supervised Learning, Unsupervised Learning, Evaluation Metrics |
| 21CS33 | Analog and Digital Electronics | Core | 4 | Diode Circuits, BJT and FET Amplifiers, Operational Amplifiers, Logic Gates, Combinational and Sequential Circuits |
| 21CSL34 | Data Structures Laboratory | Lab | 1 | Array and List Implementations, Stack and Queue Operations, Tree Traversal Algorithms, Graph Algorithms Implementation, Sorting and Searching Practical |
| 21AIMLL35 | AIML Laboratory | Lab | 1 | Python for AI/ML, Data Preprocessing, Basic ML Algorithms Implementation, Model Training and Evaluation, Data Visualization |
| 21CS36 | Discrete Mathematics | Core | 3 | Set Theory and Logic, Relations and Functions, Graph Theory, Combinatorics, Algebraic Structures |
| 21PD37 | Professional Development Skill - III | Skill Enhancement | 1 | Career Planning, Resume Building, Interview Preparation, Group Discussions, Communication Ethics |
| 21KSR38 | Vyavaharika Kannada / Constitution of India and Professional Ethics | Audit Course | 0 | Indian Constitution, Fundamental Rights, Professional Ethics, Cyber Ethics |
| 21NC39 | National Cadet Corps (NCC) / National Service Scheme (NSS) / Youth Red Cross (YRC) / Others | Audit Course | 0 | Community Service, Leadership Development, Disaster Management, Social Responsibility |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS41 | Design and Analysis of Algorithms | Core | 4 | Algorithm Efficiency, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms |
| 21AIML42 | Database Management Systems | Core | 4 | Relational Model, SQL Queries, Database Design, Transaction Management, NoSQL Databases |
| 21CS43 | Operating Systems | Core | 4 | Process Management, CPU Scheduling, Memory Management, File Systems, Deadlocks |
| 21CSL44 | Design and Analysis of Algorithms Laboratory | Lab | 1 | Implementation of Sorting Algorithms, Graph Traversal Algorithms, Dynamic Programming Problems, Greedy Algorithm Solutions, Time Complexity Analysis |
| 21AIMLL45 | Database Management Systems Laboratory | Lab | 1 | SQL Commands, Database Creation, Query Optimization, ER Modeling, PL/SQL Programming |
| 21CS46 | Probability and Statistics | Core | 3 | Probability Theory, Random Variables, Hypothesis Testing, Regression Analysis, Statistical Inference |
| 21PD47 | Professional Development Skill - IV | Skill Enhancement | 1 | Teamwork and Collaboration, Problem-solving Skills, Critical Thinking, Professional Etiquette, Time Management |
| 21KSR48 | Vyavaharika Kannada / Constitution of India and Professional Ethics | Audit Course | 0 | Indian Constitution, Fundamental Rights, Professional Ethics, Cyber Ethics |
| 21NC49 | National Cadet Corps (NCC) / National Service Scheme (NSS) / Youth Red Cross (YRC) / Others | Audit Course | 0 | Community Service, Leadership Development, Disaster Management, Social Responsibility |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS51 | Computer Networks | Core | 4 | Network Topologies, OSI and TCP/IP Models, Routing Protocols, Transport Layer Protocols, Network Security Basics |
| 21AIML52 | Machine Learning | Core | 4 | Regression Models, Classification Algorithms, Clustering Techniques, Dimensionality Reduction, Ensemble Methods |
| 21AIML53 | Artificial Neural Networks and Deep Learning | Core | 4 | Perceptrons, Multi-Layer Networks, Backpropagation, Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks |
| 21CSL54 | Computer Networks Laboratory | Lab | 1 | Socket Programming, Network Configuration, Protocol Analysis, Network Simulation, Client-Server Applications |
| 21AIMLL55 | Machine Learning Laboratory | Lab | 1 | Scikit-learn Implementation, Dataset Preparation, Model Evaluation, Hyperparameter Tuning, Building Predictive Models |
| 21CS56 | Automata Theory and Computability | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Turing Machines, Decidability and Undecidability |
| 21AIMLE57X | Professional Elective - I | Elective | 3 | Specialized AI/ML Applications, Advanced Algorithms, Domain-Specific Problem Solving, Industry-Relevant Techniques, Emerging AI Paradigms |
| 21CIE58X | Open Elective - I | Elective | 3 | Interdisciplinary topics, Skill-based courses, General interest subjects, Management principles, Social Sciences |
| 21INT59 | Internship | Practical | 2 | Real-world project experience, Industry problem exposure, Professional skill development, Team collaboration, Technical report writing |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIML61 | Big Data Analytics | Core | 4 | Big Data Concepts, Hadoop Ecosystem, Spark Framework, NoSQL Databases for Big Data, Data Stream Processing |
| 21AIML62 | Web Technologies | Core | 4 | HTML, CSS, JavaScript, Front-end Frameworks, Back-end Development (Node.js/Python), Database Integration, RESTful APIs |
| 21AIML63 | Cloud Computing | Core | 4 | Cloud Service Models (IaaS, PaaS, SaaS), Cloud Deployment Models, Virtualization, Cloud Security, Cloud Storage |
| 21AIMLL64 | Big Data Analytics Laboratory | Lab | 1 | Hadoop setup and operations, MapReduce programming, Spark application development, Data warehousing tasks, Big data tool implementation |
| 21AIMLL65 | Web Technologies Laboratory | Lab | 1 | HTML5 and CSS3 practice, JavaScript DOM manipulation, Web application development, Database connectivity, API integration |
| 21CSE66X | Professional Elective - II | Elective | 3 | Advanced AI Frameworks, IoT Integration with AI, Ethical AI Considerations, AI Model Deployment, Next-Gen Computing Paradigms |
| 21CIE67X | Open Elective - II | Elective | 3 | Interdisciplinary topics, Skill-based courses, General interest subjects, Management principles, Social Sciences |
| 21AIML68 | Mini Project | Project | 2 | Problem identification, System design, Implementation and testing, Project report writing, Presentation skills |
| 21AEC69 | Ability Enhancement Course - V (Professional Communication) | Skill Enhancement | 0 | Verbal Communication, Written Communication, Interpersonal Skills, Professional Writing, Effective Presentations |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIML71 | AI Ethics and Governance | Core | 4 | Ethical AI Principles, Bias in AI, AI Regulations and Policies, Data Privacy and Security, Fairness and Transparency in AI |
| 21CSE72X | Professional Elective - III | Elective | 3 | Application of AI in specific domains, Intelligent systems design, Data privacy and security in AI, Advanced AI algorithms, Emerging AI applications |
| 21CSE73X | Professional Elective - IV | Elective | 3 | User experience design for AI, Computational biology with AI, Secure coding practices, Image and video analytics, Distributed systems with AI |
| 21AIML74 | Internship / Project Work Phase - I | Practical/Project | 5 | Industry problem analysis, Feasibility study, Initial system design, Literature review, Project planning and management |
| 21AIML75 | Research Methodology & Intellectual Property Rights | Skill Enhancement | 3 | Research Design, Data Collection and Analysis, Patent Filing, Copyright and Trademarks, Research Ethics |
| 21AEC76 | Ability Enhancement Course - VI (Entrepreneurship and Start-ups) | Skill Enhancement | 0 | Business Idea Generation, Market Analysis, Business Plan Development, Funding Sources, Startup Ecosystem |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AIML81 | Project Work Phase - II | Project | 15 | System implementation and testing, Results analysis, Advanced problem-solving, Technical documentation, Project defense and presentation |
| 21AIML82 | Technical Seminar | Skill Enhancement | 1 | Research on advanced topics, Technical presentation skills, Public speaking, Literature survey, Q&A handling |
