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BCA in Artificial Intelligence Machine Learning at Shoolini University of Biotechnology and Management Sciences
Solan, Himachal Pradesh
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About the Specialization
What is Artificial Intelligence & Machine Learning at Shoolini University of Biotechnology and Management Sciences Solan?
This Artificial Intelligence & Machine Learning program at Shoolini University focuses on equipping students with cutting-edge skills in intelligent systems development. It covers fundamental and advanced concepts vital for India''''s rapidly growing tech landscape, aiming to produce innovators in AI-driven solutions. The program emphasizes practical application and theoretical understanding essential for modern industry demands.
Who Should Apply?
This program is ideal for fresh graduates with a strong analytical bent seeking entry into the AI/ML domain. It also suits working professionals looking to upskill in specialized areas like deep learning or natural language processing, crucial for career advancement. Students from diverse academic backgrounds with a passion for problem-solving and technology will find this curriculum engaging.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India, including AI Engineer, Machine Learning Specialist, Data Scientist, and NLP Developer. Entry-level salaries typically range from INR 4-7 lakhs per annum, with experienced professionals earning significantly more. The program prepares students for roles in startups, IT giants, and research institutions across the Indian subcontinent, fostering continuous growth.
Student Success Practices
Foundation Stage
Master Programming Fundamentals Early- (Semester 1-2)
Focus intensely on C/C++ and Data Structures. Participate in competitive programming challenges and solve daily problems to build a strong logical foundation. Understand algorithms deeply, not just superficially.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, CodeChef, local coding clubs
Career Connection
Essential for clearing technical rounds in placements, forms the base for advanced AI/ML algorithms.
Build a Strong Mathematical Base- (Semester 1-2)
Pay close attention to Mathematics for Computer Applications and Discrete Structures. These subjects are foundational for understanding AI/ML algorithms. Practice problem-solving rigorously and seek clarity on concepts like probability, statistics, and linear algebra.
Tools & Resources
Khan Academy, NPTEL courses, reference textbooks, peer study groups
Career Connection
Directly impacts comprehension of machine learning models, statistical analysis, and algorithm complexity.
Engage in Early Project-Based Learning- (Semester 1-2)
Start building small projects even in early semesters using basic programming. Apply concepts learned in labs to solve real-world mini-problems. This builds practical skills and problem-solving aptitude from the outset.
Tools & Resources
GitHub, simple Python/C++ projects, online tutorials
Career Connection
Develops a portfolio, enhances problem-solving, and prepares for larger projects later, impressing interviewers.
Intermediate Stage
Specialize through Python & AI/ML Projects- (Semester 3-5)
Dive deep into Python for AI/ML. Implement core AI/ML algorithms from scratch and use libraries like NumPy, Pandas, Scikit-learn, and TensorFlow/Keras. Contribute to open-source projects or start a personal GitHub repository with AI/ML solutions.
Tools & Resources
Kaggle, Google Colab, Jupyter Notebooks, TensorFlow, PyTorch, NLTK, OpenCV
Career Connection
Direct application of specialization knowledge, creates a robust portfolio for AI/ML specific roles.
Seek Industry Internships and Workshops- (Semester 3-5)
Actively search for summer internships in AI/ML at Indian tech companies or startups. Attend industry workshops, webinars, and hackathons focused on AI/ML. These provide practical exposure, networking opportunities, and a glimpse into corporate environments.
Tools & Resources
LinkedIn, Internshala, college placement cell, industry events
Career Connection
Bridging academic learning with industry practices, crucial for securing placements and gaining real-world experience.
Network and Participate in AI Communities- (Semester 3-5)
Join AI/ML communities, both online (e.g., Reddit''''s r/MachineLearning, Indian AI forums) and offline (college clubs, local meetups). Collaborate with peers on projects, discuss new research, and learn from experienced professionals.
Tools & Resources
LinkedIn, GitHub, specific AI/ML Discord/Slack channels, college AI clubs
Career Connection
Expands professional network, leads to collaborative opportunities, and provides insights into industry trends and job openings.
Advanced Stage
Develop a Capstone Project with Impact- (Semester 6)
Undertake a significant Major Project (BCS-PRJ601) that solves a real-world problem using advanced AI/ML techniques. Aim for innovation and demonstrate a clear understanding of the entire project lifecycle, from problem definition to deployment.
Tools & Resources
Cloud platforms (AWS, Azure, GCP), advanced ML frameworks, GitHub, project management tools
Career Connection
Serves as the centerpiece of your portfolio, showcasing expertise and problem-solving abilities to potential employers, critical for senior roles or research.
Master Advanced AI/ML Concepts & Tools- (Semester 6)
Beyond core curriculum, delve into advanced topics like MLOps, explainable AI (XAI), advanced deep learning architectures, or specialized NLP/CV techniques. Gain hands-on experience with industry-standard tools and platforms for deployment and monitoring.
Tools & Resources
Coursera, edX, Udemy for advanced courses, Hugging Face, Weights & Biases, MLflow
Career Connection
Makes you highly competitive for specialized roles, showcasing proactive learning and staying updated with industry demands.
Prepare for Placements and Career Launch- (Semester 6)
Systematically prepare for interviews, focusing on data structures, algorithms, AI/ML concepts, and project discussions. Practice mock interviews, refine your resume/CV, and build a strong LinkedIn profile. Research target companies and their tech stacks.
Tools & Resources
InterviewBit, LeetCode, Glassdoor, professional resume services, college placement cell
Career Connection
Directly impacts successful placement in top companies, ensuring a smooth transition from academics to a professional AI/ML career.
Program Structure and Curriculum
Eligibility:
- 10+2 with minimum 50% marks (Any Stream) with Maths/Computer Science/Information Practice/IT as one of the subjects
Duration: 3 years / 6 semesters
Credits: 127 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCS-C101 | Programming in C | Core | 3 | Introduction to C, Operators and Expressions, Control Flow Statements, Functions and Pointers, Arrays and Strings, Structures and Unions |
| BCS-C102 | Data Base Management System | Core | 3 | Database Concepts, ER Model, Relational Model, SQL Queries, Normalization, Transaction Management |
| BCS-C103 | Digital Electronics | Core | 3 | Number Systems, Logic Gates, Boolean Algebra, Combinational Circuits, Sequential Circuits, Registers and Counters |
| BSC-C104 | Mathematics for Computer Applications | Core | 3 | Set Theory, Logic and Propositional Calculus, Matrices and Determinants, Graph Theory Basics, Probability Distributions, Statistical Methods |
| HSS-C101 | Professional Communication | Core | 2 | Communication Process, Verbal and Non-verbal Communication, Active Listening, Presentation Skills, Business Correspondence, Report Writing |
| BCS-L101 | Programming in C Lab | Lab | 2 | C Program Development, Conditional Statements, Looping Constructs, Function Implementation, Array and String Operations, Pointer Usage |
| BCS-L102 | Data Base Management System Lab | Lab | 2 | SQL Commands, Database Creation and Manipulation, Table Joins, Views and Stored Procedures, Data Definition Language, Data Manipulation Language |
| BCS-L103 | Digital Electronics Lab | Lab | 2 | Logic Gate Verification, Boolean Algebra Implementation, Adder/Subtractor Circuits, Flip-Flops, Counters Design, Multiplexers and Demultiplexers |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCS-C201 | Data Structures | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Binary Search Trees, Graphs and Traversals, Searching Algorithms, Sorting Algorithms |
| BCS-C202 | Object Oriented Programming using C++ | Core | 3 | OOP Concepts, Classes and Objects, Inheritance, Polymorphism, Operator Overloading, Exception Handling |
| BCS-C203 | Computer System Architecture | Core | 3 | CPU Organization, Memory Hierarchy, Input/Output Organization, Instruction Set Architecture, Pipelining, Parallel Processing |
| BCS-C204 | Operating System | Core | 3 | OS Introduction, Process Management, CPU Scheduling, Memory Management, File Systems, Deadlocks |
| BSC-C205 | Basic Computer Networks | Core | 3 | Network Topologies, OSI and TCP/IP Models, Network Devices, Data Link Layer, Network Layer Protocols, Transport Layer |
| BCS-L201 | Data Structures Lab | Lab | 2 | Implementation of Linked Lists, Stack and Queue Operations, Tree Traversals, Graph Algorithms, Sorting Techniques, Searching Techniques |
| BCS-L202 | Object Oriented Programming using C++ Lab | Lab | 2 | Class and Object Design, Inheritance Scenarios, Polymorphism Implementation, Constructor Overloading, File Handling in C++, Exception Handling Practice |
| BCS-L203 | Operating System Lab | Lab | 2 | Linux Commands, Shell Scripting, Process Management Utilities, CPU Scheduling Simulation, Memory Management Techniques, File System Operations |
| BCS-L204 | Basic Computer Networks Lab | Lab | 2 | Network Configuration, IP Addressing and Subnetting, Network Monitoring Tools, Socket Programming, Firewall Rules, Troubleshooting Network Issues |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCS-C301 | Computer Based Optimization Techniques | Core | 3 | Linear Programming, Simplex Method, Transportation Problem, Assignment Problem, Network Flow Problems, Dynamic Programming |
| BCS-C302 | Introduction to Python Programming | Core | 3 | Python Fundamentals, Data Types and Structures, Control Flow, Functions and Modules, File Handling, Object-Oriented Python |
| BCS-C303 | Artificial Intelligence | Core | 3 | Introduction to AI, Problem Solving Agents, Search Algorithms, Knowledge Representation, Expert Systems, Machine Learning Basics |
| BCS-C304 | Discrete Structures | Core | 3 | Set Theory and Relations, Mathematical Logic, Functions, Counting Techniques, Graph Theory, Algebraic Structures |
| BCS-C305 | Web Technology | Core | 3 | HTML Fundamentals, CSS Styling, JavaScript Basics, Web Server Concepts, Client-Server Architecture, Introduction to PHP |
| BCS-L301 | Python Programming Lab | Lab | 2 | Python Scripting, Data Structure Manipulation, File Operations, Web Scraping, GUI Development, Database Connectivity |
| BCS-L302 | Artificial Intelligence Lab | Lab | 2 | AI Programming in Python, Search Algorithm Implementation, Logic Programming, Knowledge Representation Systems, Expert System Development, Simple Machine Learning Models |
| BCS-L303 | Web Technology Lab | Lab | 2 | Static Web Page Design, Dynamic Styling with CSS, Client-side Scripting with JavaScript, Form Handling with PHP, Database Integration with Web, Responsive Web Design |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCS-C401 | Computer Graphics | Core | 3 | Graphics Primitives, 2D Transformations, 3D Transformations, Clipping Algorithms, Projections, Shading and Rendering |
| BCS-C402 | Software Engineering | Core | 3 | Software Development Life Cycle, Requirements Engineering, Software Design, Software Testing, Software Maintenance, Project Management Concepts |
| BCS-C403 | Data Warehousing & Data Mining | Core | 3 | Data Warehouse Architecture, ETL Process, OLAP Operations, Data Mining Techniques, Association Rule Mining, Classification and Clustering |
| BCS-C404 | Machine Learning | Core | 3 | ML Fundamentals, Supervised Learning, Unsupervised Learning, Regression Models, Classification Algorithms, Neural Network Basics |
| BCS-C405 | Big Data Analytics | Core | 3 | Big Data Concepts, Hadoop Ecosystem, HDFS, MapReduce, Apache Spark, Data Stream Processing |
| BCS-L401 | Computer Graphics Lab | Lab | 2 | Drawing Graphics Primitives, Implementing Transformations, Interactive Graphics Programming, Coloring and Shading, Animation Techniques, 3D Object Manipulation |
| BCS-L402 | Software Engineering Lab | Lab | 2 | UML Diagramming, Requirement Gathering Tools, Software Design Patterns, Testing Frameworks, Version Control Systems, Project Planning Tools |
| BCS-L403 | Machine Learning Lab | Lab | 2 | Data Preprocessing, Supervised Learning Algorithms, Unsupervised Learning Algorithms, Model Training and Evaluation, Feature Engineering, Hyperparameter Tuning |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCS-C501 | Theory of Computation | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Pushdown Automata, Turing Machines, Decidability and Undecidability |
| BCS-C502 | Computer Security | Core | 3 | Cryptography Principles, Network Security, Cyberattacks and Countermeasures, Firewalls and IDS/IPS, Security Policies, Vulnerability Assessment |
| BCS-C503 | Natural Language Processing | Core | 3 | NLP Introduction, Text Preprocessing, N-grams and Language Models, Part-of-Speech Tagging, Sentiment Analysis, Machine Translation Concepts |
| BCS-DSE501 | Deep Learning | Elective | 3 | Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Backpropagation Algorithm, Optimization Techniques, Generative Adversarial Networks |
| BCS-DSE502 | Reinforcement Learning | Elective | 3 | RL Fundamentals, Markov Decision Processes, Q-Learning, Policy Gradient Methods, Deep Reinforcement Learning, Exploration-Exploitation Tradeoff |
| BCS-L501 | Computer Security Lab | Lab | 2 | Encryption and Decryption, Digital Signatures, Network Scanning Tools, Intrusion Detection Systems, Setting up Firewalls, Secure Coding Practices |
| BCS-L502 | Natural Language Processing Lab | Lab | 2 | NLP Libraries (NLTK, SpaCy), Text Tokenization and Stemming, Named Entity Recognition, Topic Modeling, Chatbot Development, Text Summarization |
| BCS-L503 | Deep Learning Lab | Lab | 2 | Building Neural Networks with Keras/PyTorch, Image Classification, Sequence Prediction, Transfer Learning, Data Augmentation, Model Evaluation Metrics |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCS-C601 | Mobile Application Development | Core | 3 | Android/iOS Architecture, UI/UX Design Principles, Activities and Intents, Data Storage in Mobile Apps, API Integration, Deployment to App Stores |
| BCS-C602 | Cloud Computing | Core | 3 | Cloud Computing Models (IaaS, PaaS, SaaS), Deployment Models, Virtualization Technology, Cloud Storage, Cloud Security, Cloud Migration Strategies |
| BCS-DSE601 | Computer Vision | Elective | 3 | Image Processing Fundamentals, Feature Extraction, Object Detection, Image Segmentation, Facial Recognition, Deep Learning for Vision |
| BCS-PRJ601 | Major Project | Project | 8 | Problem Identification, System Design, Implementation and Testing, Project Management, Documentation and Reporting, Presentation and Evaluation |
| BCS-L601 | Mobile Application Development Lab | Lab | 2 | Developing Android Apps, Designing User Interfaces, Handling User Input, Database Integration in Apps, Location-based Services, Push Notifications |
| BCS-L602 | Cloud Computing Lab | Lab | 2 | Deploying Virtual Machines, Managing Cloud Storage, Using AWS/Azure Services, Serverless Computing, Containerization with Docker, Monitoring Cloud Resources |
