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BE in Artificial Intelligence And Machine Learning at Dayananda Sagar Academy of Technology and Management
Bengaluru, Karnataka
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About the Specialization
What is Artificial Intelligence and Machine Learning at Dayananda Sagar Academy of Technology and Management Bengaluru?
This Artificial Intelligence and Machine Learning program at Dayananda Sagar Academy of Technology and Management focuses on equipping students with advanced knowledge and practical skills in AI, ML, and Data Science. With India''''s rapid digital transformation, there''''s immense demand for professionals who can innovate and deploy intelligent solutions. This program emphasizes a strong theoretical foundation coupled with hands-on project experience, preparing graduates for cutting-edge roles in various industries.
Who Should Apply?
This program is ideal for aspiring engineers and innovators passionate about technology and its applications. It attracts fresh 10+2 graduates seeking entry into the high-growth fields of AI and ML, as well as working professionals aiming to upskill for leadership roles in data-driven decision-making. Individuals with a strong aptitude for mathematics, programming, and problem-solving, looking to build a career in designing intelligent systems, will thrive in this curriculum.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India, including AI Engineer, Machine Learning Scientist, Data Scientist, Business Intelligence Analyst, and Robotics Engineer. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning INR 15-30+ LPA. The curriculum aligns with certifications like TensorFlow Developer and AWS Certified Machine Learning Specialist, enabling rapid professional growth in Indian and global tech companies.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Focus rigorously on C and Python programming. Utilize platforms like HackerRank and CodeChef for daily coding challenges to build a strong logical foundation. This proficiency is crucial for all advanced AI/ML courses and will significantly enhance performance in technical interviews during placements.
Tools & Resources
HackerRank, CodeChef, GeeksforGeeks
Career Connection
Strong programming skills are foundational for software development roles and ace technical coding rounds in placement drives.
Excel in Mathematics- (Semester 1-2)
Pay close attention to Engineering Mathematics and Probability & Statistics. Use online resources like Khan Academy and NPTEL lectures to solidify concepts. A strong mathematical base is indispensable for understanding ML algorithms, and mastering these subjects early will ease the learning curve for complex topics later.
Tools & Resources
Khan Academy, NPTEL, MIT OpenCourseWare
Career Connection
Essential for understanding algorithm mechanics, data modeling, and excelling in quantitative roles in AI/ML research and development.
Build a Peer Learning Network- (Semester 1-2)
Form study groups to discuss concepts, solve problems collaboratively, and prepare for exams. Engage with seniors for guidance on course selection, project ideas, and career paths. This fosters a supportive environment and exposes you to diverse perspectives, improving overall academic and social development.
Tools & Resources
College study groups, Online forums, LinkedIn
Career Connection
Develops teamwork, communication, and networking skills, which are highly valued in professional environments and for collaborative projects.
Intermediate Stage
Apply Theoretical Knowledge to Projects- (Semester 3-5)
Actively seek out opportunities to work on mini-projects using Python, Java, and ML libraries. Platforms like Kaggle offer real-world datasets for practice. Hands-on application reinforces learning, builds a portfolio, and demonstrates practical skills to potential employers for internships and jobs.
Tools & Resources
Kaggle, GitHub, Scikit-learn, TensorFlow/PyTorch
Career Connection
Creates a tangible portfolio of work, making you more competitive for internships and entry-level positions by showcasing practical problem-solving abilities.
Gain Early Industry Exposure- (Semester 3-5)
Complete relevant online courses or certifications in AI/ML (e.g., from Coursera, edX, NPTEL). Attend workshops, webinars, and tech events organized by the department or local tech communities. This keeps you updated with industry trends and helps identify areas of interest for specialization.
Tools & Resources
Coursera, edX, NPTEL, Local tech meetups (e.g., AI Bengaluru)
Career Connection
Develops industry awareness, helps in choosing career paths, and provides talking points in interviews, demonstrating initiative and specialized knowledge.
Participate in Hackathons & Competitions- (Semester 3-5)
Join hackathons and coding competitions organized by the college or external bodies. These events challenge your problem-solving skills under pressure, foster teamwork, and provide excellent networking opportunities. Winning or even participating actively enhances your resume and showcases initiative.
Tools & Resources
Devpost, Major League Hacking (MLH), College coding clubs
Career Connection
Builds resilience, problem-solving under pressure, and teamwork. Successful participation can lead to direct interview opportunities and valuable industry connections.
Advanced Stage
Undertake Significant Capstone Projects- (Semester 6-8)
Focus on developing a substantial final year project that addresses a real-world problem, potentially collaborating with industry. Aim for innovative solutions and a high-quality implementation. A strong capstone project is a critical talking point in interviews and a testament to your specialized skills.
Tools & Resources
Research papers, Industry mentors, Cloud platforms (AWS, Azure, GCP), Project management tools
Career Connection
Showcases advanced technical skills, problem-solving capabilities, and can be a direct path to employment through industry partnerships or startup ventures.
Prepare Strategically for Placements- (Semester 6-8)
Start early with dedicated aptitude training, mock interviews, and resume building workshops. Leverage the college''''s placement cell for company-specific preparation and mock group discussions. Focus on refining both technical and soft skills to secure top-tier placements in desired AI/ML roles.
Tools & Resources
Placement Cell resources, Online aptitude tests, Interview prep platforms (e.g., LeetCode, InterviewBit)
Career Connection
Maximizes chances of securing high-quality placements in leading technology companies, aligning with career aspirations and salary expectations.
Specialize and Network Professionally- (Semester 6-8)
Choose professional electives wisely to deepen expertise in an AI/ML sub-field (e.g., NLP, Computer Vision). Attend conferences, connect with alumni, and build a professional presence on platforms like LinkedIn. Networking opens doors to mentorship, job opportunities, and staying abreast of industry advancements.
Tools & Resources
LinkedIn, Industry conferences (e.g., Data Science Congress), Professional organizations
Career Connection
Establishes a professional network, facilitates mentorship, and provides access to exclusive job opportunities and insights into advanced career trajectories.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 examination with Physics, Mathematics, and any one of Chemistry/Biology/Biotechnology/Computer Science/Electronics as optional subjects with English as one of the languages of study. Minimum 45% marks (40% for reserved categories) in the mentioned optional subjects. Must have appeared for entrance exams like KCET/COMEDK/JEE Main.
Duration: 8 semesters / 4 years
Credits: 150 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21MATS11 | Engineering Mathematics-I | Core | 3 | Differential Calculus, Integral Calculus, Multivariable Calculus, Vector Calculus, Ordinary Differential Equations |
| 21PCD12 | Programming for Problem Solving | Core | 3 | Introduction to C, Operators & Expressions, Control Structures, Functions, Arrays & Strings, Pointers |
| 21ELE13 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Transformers, DC Machines, AC Machines, Power Systems |
| 21CIV14 | Basic Civil Engineering | Core | 3 | Building Materials, Surveying, Concrete Technology, Structural Elements, Water Resources, Transportation |
| 21EGDL15 | Engineering Graphics and Design Laboratory | Lab | 2 | Orthographic Projections, Isometric Projections, Sectional Views, Development of Surfaces, AutoCAD |
| 21PCDL16 | Programming for Problem Solving Laboratory | Lab | 1 | C programming exercises, Conditional statements, Loops, Arrays, Functions, Pointers, File I/O |
| 21EEL17 | Basic Electrical Engineering Laboratory | Lab | 1 | Verification of Circuit Laws, Measurement of Electrical Quantities, Motor Characteristics, Transformer tests |
| 21CIP18 | Python Programming | Skill | 1 | Python basics, Data types, Control structures, Functions, Modules, File handling |
| 21EV19 | Environmental Studies | Core | 1 | Ecosystems, Environmental Pollution, Global Environmental Issues, Sustainable Development, Environmental Legislation, Waste Management |
| 21KSK29 | Communicative Kannada / Vyavaharika Kannada | Core | 1 | Basic Kannada grammar, Conversational Kannada, Reading & Writing skills, Kannada culture and heritage |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21MATS21 | Engineering Mathematics-II | Core | 3 | Linear Algebra, Vector Spaces, Eigenvalues & Eigenvectors, Laplace Transforms, Fourier Series, Z-Transforms |
| 21ECL22 | Basic Electronics | Core | 3 | Semiconductor Diodes, Transistors, Operational Amplifiers, Digital Electronics, Communication Systems, IoT Introduction |
| 21ME23 | Basic Mechanical Engineering | Core | 3 | Thermodynamics, IC Engines, Refrigeration, Power Transmission, Material Science, Manufacturing Processes |
| 21CHY24 | Engineering Chemistry | Core | 3 | Electrochemistry, Corrosion, Fuel Chemistry, Water Technology, Polymer Science, Nanomaterials |
| 21CHYL25 | Engineering Chemistry Laboratory | Lab | 1 | Water analysis, Acid-base titrations, Viscosity, Surface tension, Spectrophotometry experiments |
| 21ECL26 | Basic Electronics Laboratory | Lab | 1 | Diode characteristics, Rectifiers, Transistor amplifier, Logic gates, Op-Amp applications, Sensor interfacing |
| 21CPL27 | Computer Aided Product Design & Manufacturing Laboratory | Lab | 2 | CAD software exercises, 3D part modeling, Assembly creation, Drafting, CAM simulation, CNC programming basics |
| 21PDM28 | Professional Development & Management | Core | 1 | Communication Skills, Personality Development, Goal Setting, Time Management, Ethics, Entrepreneurship |
| 21CIP29 | C Programming for Engineers | Skill | 1 | Advanced C concepts, Data structures in C, File management, Pointers, Dynamic memory allocation |
| 21KSK29 | Communicative Kannada / Vyavaharika Kannada | Core | 1 | Advanced Kannada conversations, Cultural aspects, Literature introduction, Formal communication, Translation |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS31 | Discrete Mathematics | Core | 3 | Set Theory, Logic, Relations & Functions, Graph Theory, Number Theory, Counting Techniques |
| 21CS32 | Data Structures and Applications | Core | 4 | Arrays, Stacks, Queues, Linked Lists, Trees, Graphs, Sorting, Searching |
| 21AI33 | Object Oriented Programming with Java | Core | 3 | Classes & Objects, Inheritance, Polymorphism, Interfaces, Exception Handling, Multithreading |
| 21AI34 | Database Management Systems | Core | 3 | Data Models, SQL, Relational Algebra, Normalization, Transactions, Concurrency Control |
| 21AI35 | Introduction to Artificial Intelligence | Core | 3 | AI History, Intelligent Agents, Problem Solving, Search Algorithms, Knowledge Representation, Machine Learning Basics |
| 21CSL36 | Data Structures Laboratory | Lab | 1 | Implementations of stacks, queues, linked lists, trees, sorting algorithms |
| 21AIL37 | Object Oriented Programming with Java Laboratory | Lab | 1 | Java programs for classes, objects, inheritance, polymorphism, file I/O |
| 21AIH38 | Python Programming Laboratory | Lab | 1 | Python basics, Data structures, Functions, Modules, File handling, Object-Oriented Programming in Python |
| 21AI39 | Skill Lab - Web Stack Development | Skill | 1 | HTML, CSS, JavaScript, Web frameworks, Front-end development, Back-end basics |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS41 | Design and Analysis of Algorithms | Core | 4 | Algorithm analysis, Divide & Conquer, Greedy algorithms, Dynamic programming, Graph algorithms, NP-Completeness |
| 21AI42 | Operating Systems | Core | 3 | Process Management, CPU Scheduling, Memory Management, Virtual Memory, File Systems, I/O Systems |
| 21AI43 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Regression, Classification, Clustering, Model Evaluation, Ensemble Methods |
| 21AI44 | Probability and Statistics for AI/ML | Core | 3 | Probability Theory, Random Variables, Distributions, Hypothesis Testing, Regression Analysis, Correlation |
| 21AI45 | Introduction to Data Science | Core | 3 | Data collection, Data preprocessing, Exploratory Data Analysis, Data Visualization, Data storytelling, Big Data Introduction |
| 21CSL46 | Operating Systems Laboratory | Lab | 1 | Linux commands, Shell scripting, Process synchronization, Deadlock prevention, Memory allocation |
| 21AIL47 | Machine Learning Laboratory | Lab | 1 | Implementations of ML algorithms, Data preprocessing, Model training, Evaluation using Python libraries |
| 21AI48 | Skill Lab - R Programming | Skill | 1 | R data structures, Functions, Data manipulation, Data visualization, Statistical analysis in R |
| 21AI49 | Mini Project | Project | 2 | Problem definition, System design, Implementation, Testing, Project Report, Presentation |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AI51 | Automata Theory and Computability | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Pushdown Automata, Turing Machines, Undecidability |
| 21AI52 | Deep Learning | Core | 4 | Neural Networks, Backpropagation, Convolutional Neural Networks, Recurrent Neural Networks, Transformers, Generative Models |
| 21AI53 | Computer Networks | Core | 3 | Network Topologies, OSI/TCP-IP Models, IP Addressing, Routing Protocols, Transport Layer, Application Layer Protocols |
| 21AI54 | Professional Elective - 1 | Elective | 3 | Varies based on elective chosen (e.g., Natural Language Processing, Computer Vision, Reinforcement Learning) |
| 21AI55 | Open Elective - 1 | Elective | 3 | Varies based on elective chosen (e.g., IoT, Blockchain, Entrepreneurship) |
| 21AIL56 | Deep Learning Laboratory | Lab | 1 | Building and training CNNs, RNNs, other deep learning models, TensorFlow/PyTorch frameworks |
| 21AIL57 | Computer Networks Laboratory | Lab | 1 | Network configuration, Socket programming, Protocol analysis, Network security tools |
| 21AI58 | Universal Human Values | Core | 1 | Understanding Self, Family, Society, Ethics, Holistic Vision, Professional Values |
| 21AIC59 | Constitution of India, Professional Ethics and Cyber Law | Core | 1 | Indian Constitution, Fundamental Rights, Duties, Professional Ethics, Cyber Crime, IT Act |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AI61 | Applied Machine Learning | Core | 4 | Feature Engineering, Model Deployment, MLOps, Explainable AI, Anomaly Detection, Time Series Analysis |
| 21AI62 | Big Data Analytics | Core | 3 | Hadoop Ecosystem, MapReduce, Spark, NoSQL Databases, Data Warehousing, Data Streaming |
| 21AI63 | Data Visualization | Core | 3 | Principles of Visualization, Data Storytelling, Visualization Tools, Interactive Dashboards, Information Graphics |
| 21AI64 | Professional Elective - 2 | Elective | 3 | Varies based on elective chosen |
| 21AI65 | Open Elective - 2 | Elective | 3 | Varies based on elective chosen |
| 21AIL66 | Applied Machine Learning Laboratory | Lab | 1 | Real-world ML project, Feature engineering, Model optimization, Deployment strategies |
| 21AIL67 | Big Data Analytics Laboratory | Lab | 1 | Hadoop/Spark implementation, Data processing, Querying NoSQL databases, Distributed computing |
| 21AI68 | Technical Seminar | Project | 1 | Research paper presentation, Technical writing, Communication skills, Latest technologies, Literature review |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AI71 | Reinforcement Learning | Core | 4 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal Difference Learning, Policy Gradients, Deep Reinforcement Learning |
| 21AI72 | Professional Elective - 3 | Elective | 3 | Varies based on elective chosen |
| 21AI73 | Professional Elective - 4 | Elective | 3 | Varies based on elective chosen |
| 21AI74 | Open Elective - 3 | Elective | 3 | Varies based on elective chosen |
| 21AIL75 | Reinforcement Learning Laboratory | Lab | 1 | Implementing RL algorithms, OpenAI Gym environments, Policy optimization, Value-based methods |
| 21AI76 | Project Work Phase - 1 | Project | 4 | Problem identification, Literature survey, Design, Methodology, Preliminary results, Project planning |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21AI81 | Professional Elective - 5 | Elective | 3 | Varies based on elective chosen |
| 21AI82 | Internship / Project Work | Project | 9 | Industry internship experience, Comprehensive project development, Product deployment, Technical documentation, Presentation, Project management |
