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B-TECH in Artificial Intelligence Machine Learning at ST. JOSEPH ENGINEERING COLLEGE
Dakshina Kannada, Karnataka
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About the Specialization
What is Artificial Intelligence & Machine Learning at ST. JOSEPH ENGINEERING COLLEGE Dakshina Kannada?
This Artificial Intelligence & Machine Learning program at St Joseph Engineering College focuses on equipping students with deep knowledge and practical skills in AI, ML, and their applications. It is meticulously designed to meet the escalating demand for AI professionals in India, covering core concepts from data structures to deep learning, with a strong emphasis on problem-solving. The curriculum differentiates itself by integrating cutting-edge tools and methodologies relevant to contemporary industry needs in the Indian market.
Who Should Apply?
This program is ideal for fresh graduates seeking entry into the high-growth fields of AI/ML, working professionals looking to upskill in advanced computational techniques, and career changers transitioning to the rapidly expanding AI industry. Candidates typically possess a strong foundation in mathematics and basic programming skills, demonstrating an aptitude for logical thinking and complex problem-solving.
Why Choose This Course?
Graduates of this program can expect to secure lucrative career paths such as AI Engineers, Machine Learning Scientists, Data Scientists, and AI Researchers within leading Indian tech companies and startups. Entry-level salaries typically range from INR 6-10 LPA, with experienced professionals potentially earning INR 15-30+ LPA. The program aligns with professional certifications from platforms like Google, AWS, and Microsoft, fostering continuous growth trajectories in the dynamic Indian job market.
Student Success Practices
Foundation Stage
Master Programming Fundamentals with C and Python- (Semester 1-2)
Dedicate significant time in semesters 1 and 2 to build strong logical programming skills using C, followed by Python. Actively participate in coding contests, solve problems on platforms like HackerRank and LeetCode, and join college coding clubs to enhance problem-solving abilities. Focus on understanding data structures and algorithms conceptually and practically.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Jupyter Notebook, VS Code
Career Connection
A robust foundation in programming is crucial for all AI/ML roles, serving as the bedrock for developing sophisticated algorithms and efficient software solutions, directly impacting eligibility for core technical roles in placements.
Build a Strong Mathematical and Statistical Base- (Semester 1-2)
Focus intensely on Calculus, Linear Algebra, Probability, and Statistics during the initial semesters. These subjects form the theoretical backbone of AI/ML. Supplement classroom learning with online courses from NPTEL, Khan Academy, or Coursera to gain a deeper understanding. Practice problem-solving rigorously to internalize concepts.
Tools & Resources
NPTEL courses, Khan Academy, Coursera (Mathematics for ML specialization), Textbooks
Career Connection
A deep understanding of mathematics and statistics is essential for comprehending ML algorithms, optimizing models, and interpreting results, which are core competencies for Data Scientists and ML Engineers.
Develop Effective Study Habits and Peer Learning- (Semester 1-2)
Form study groups to discuss complex topics, share resources, and collectively solve problems. Practice active recall and spaced repetition techniques for better retention. Utilize the library resources and academic support systems offered by the college. Engage with faculty during office hours for clarifications and deeper insights.
Tools & Resources
College Library, Study Groups, Faculty Mentorship
Career Connection
Strong academic performance and collaborative skills developed through peer learning enhance overall profile, leading to better internship and placement opportunities, and building a professional network early on.
Intermediate Stage
Engage in Project-Based Learning and Internships- (Semester 3-5)
Starting from semester 3, actively seek out and participate in projects (academic, personal, or open-source) related to Data Structures, DBMS, and Object-Oriented Programming. Aim for at least one summer internship after the 4th or 6th semester in a relevant domain. This provides practical application of theoretical knowledge.
Tools & Resources
GitHub, Kaggle, LinkedIn for internships, Company career pages
Career Connection
Practical project experience and internships are critical for demonstrating real-world problem-solving skills, building a portfolio, and securing competitive placements in core AI/ML companies.
Specialize in Core AI/ML Technologies- (Semester 3-5)
As core AI/ML subjects (Machine Learning, Data Mining) are introduced, dive deep into Python libraries like Scikit-learn, Pandas, NumPy, and Matplotlib. Work on mini-projects that involve data preprocessing, model training, and evaluation. Participate in hackathons focused on AI/ML problems to test and expand your skills.
Tools & Resources
Anaconda Navigator, Google Colab, Kaggle Competitions, GitHub
Career Connection
Specialized skills in popular AI/ML frameworks and libraries are highly valued by employers, directly influencing your suitability for roles as ML Engineers, Data Analysts, or AI Developers.
Build a Professional Network and Soft Skills- (Semester 3-5)
Attend workshops, seminars, and conferences related to AI/ML. Connect with industry professionals and alumni on platforms like LinkedIn. Focus on developing presentation skills, teamwork, and communication through group projects and extracurricular activities. Good soft skills are often a differentiator in hiring.
Tools & Resources
LinkedIn, College Career Fairs, Toastmasters (if available)
Career Connection
Networking opens doors to hidden job opportunities and mentorship. Strong soft skills ensure you can articulate your technical knowledge effectively, crucial for interviews and professional growth in Indian companies.
Advanced Stage
Master Deep Learning and Advanced AI Concepts- (Semester 6-8)
In semesters 6-8, focus on Deep Learning, Reinforcement Learning, and Optimization for ML. Work extensively with TensorFlow, Keras, and PyTorch. Develop complex AI projects, potentially contributing to open-source initiatives or participating in advanced research projects within the college.
Tools & Resources
TensorFlow, PyTorch, Keras, AWS/Azure/GCP ML services
Career Connection
Expertise in deep learning and advanced AI is essential for roles in cutting-edge AI research, computer vision, NLP, and high-performance computing, offering top-tier salaries in Indian tech giants and startups.
Undertake a Capstone Project and Publish Research- (Semester 6-8)
Utilize the Project Work phases (Semester 7 & 8) to develop a significant, industry-relevant AI/ML solution. Aim for publication in college journals or presentations at student conferences. This demonstrates advanced problem-solving, research capabilities, and technical depth.
Tools & Resources
Academic Journals, ResearchGate, Conference proceedings, Project management tools
Career Connection
A strong capstone project and any published work significantly boost your resume, providing tangible evidence of your abilities for specialized roles and for higher studies (M.Tech/Ph.D.) both in India and abroad.
Focus on Placement Preparation and Career Strategy- (Semester 6-8)
Begin rigorous preparation for placements by semester 7. Practice mock interviews, group discussions, and aptitude tests. Tailor your resume and cover letter to specific job descriptions. Research companies and roles aligned with your specialization. Leverage the college''''s placement cell resources thoroughly.
Tools & Resources
Placement Cell, Mock Interview Platforms, Company-specific interview guides, LinkedIn
Career Connection
Dedicated and strategic placement preparation is key to converting technical skills into a successful career, leading to highly sought-after positions in the Indian IT and analytics industries.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 or equivalent examination with English as one of the languages and obtained a minimum of 45% of marks in aggregate in Physics and Mathematics along with one of the Chemistry/Biotechnology/Biology/Electronics/Computer/Technical Vocational subject. For SC/ST and Category-I, 40% aggregate marks. Should appear for competitive exams like KCET/COMEDK/JEE.
Duration: 8 semesters / 4 years
Credits: 160 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BMAES101 | Calculus and Differential Equations | Core | 4 | Differential Calculus, Integral Calculus, Multivariable Calculus, Differential Equations, Vector Calculus |
| BCES102 | Programming for Problem Solving | Core | 4 | C Programming Fundamentals, Control Structures, Arrays and Strings, Functions and Pointers, Structures and File Handling |
| BEES103 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Transformers, Electrical Machines, Power Systems |
| BHUES104 | Technical English | Core | 2 | Communication Skills, Technical Writing, Presentation Techniques, Reading Comprehension, Listening Skills |
| BCES105 | Engineering Graphics | Core | 3 | Orthographic Projections, Isometric Projections, Sectional Views, Development of Surfaces, AutoCAD Basics |
| BCES106 | Programming for Problem Solving Lab | Lab | 2 | C Programming Practice, Conditional Statements, Loops and Arrays, Functions and Pointers, File Operations |
| BEES107 | Basic Electrical Engineering Lab | Lab | 1 | Verification of Circuit Laws, AC Circuit Analysis, Transformer Characteristics, DC Machine Experiments, Household Wiring |
| BHUES108 | Environmental Science and Sustainability | Core | 1 | Ecosystems, Biodiversity, Pollution and Control, Renewable Energy, Sustainable Development |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BMAES201 | Advanced Calculus and Numerical Methods | Core | 4 | Linear Algebra, Laplace Transforms, Fourier Series, Numerical Methods, Partial Differential Equations |
| BPES202 | Engineering Physics | Core | 4 | Quantum Mechanics, Laser and Optical Fibers, Material Science, Semiconductor Physics, Nanotechnology |
| BESES203 | Elements of Civil Engineering and Mechanics | Core | 3 | Building Materials, Surveying, Mechanics of Materials, Fluid Mechanics, Environmental Impact |
| BCES204 | Computer Aided Engineering Drawing | Core | 3 | CAD Software Basics, Geometric Constructions, Projections of Solids, Sectional Views in CAD, Assembly Drawings |
| BHUES205 | Scientific Foundations of Health | Core | 1 | Human Anatomy, Physiology, Nutrition and Diet, Yoga and Stress Management, Basic First Aid |
| BPES206 | Engineering Physics Lab | Lab | 1 | Laser Wavelength Measurement, Optical Fiber Losses, Semiconductor Diode Characteristics, Dielectric Constant, Magnetic Field Measurement |
| BCES207 | Computer Aided Engineering Workshop | Lab | 1 | Workshop Safety, Fitting, Carpentry, Welding, Sheet Metal |
| BHUES208 | Professional Development and Ethics | Core | 1 | Professionalism, Ethics in Engineering, Teamwork, Leadership, Communication Skills |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BMAC301 | Discrete Mathematical Structures | Core | 3 | Set Theory and Logic, Relations and Functions, Graph Theory, Algebraic Structures, Combinatorics and Probability |
| BAIC302 | Data Structures and Applications | Core | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Heaps, Graphs and Hashing, Sorting and Searching Algorithms |
| BAIC303 | Analog and Digital Electronics | Core | 3 | Diodes and Transistors, Operational Amplifiers, Logic Gates, Combinational Logic, Sequential Logic Circuits |
| BAIC304 | Database Management Systems | Core | 4 | ER Model, Relational Model, SQL Queries, Normalization, Transaction Management |
| BAIC305 | Object Oriented Programming with JAVA | Core | 4 | Classes and Objects, Inheritance and Polymorphism, Interfaces and Packages, Exception Handling, Multithreading |
| BAIC306 | Data Structures and DBMS Lab | Lab | 2 | Implementation of DS, C/C++ Programming, SQL Queries, Database Design, Procedural SQL |
| BAIC307 | Analog and Digital Electronics Lab | Lab | 1 | Diode Characteristics, Transistor Amplifiers, Logic Gates, Flip-flops and Counters, ADC/DAC experiments |
| BAIC308 | Skill Development Course - 1 (Python Programming) | Skill Development | 1 | Python Basics, Data Types, Control Flow, Functions, Modules |
| BBLC309 | Biology for Engineers | Mandatory Non-Credit | 0 | Basic Biology, Human Body Systems, Genetics, Ecology, Bioethics |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BMAC401 | Probability and Statistics | Core | 3 | Probability Theory, Random Variables, Statistical Distributions, Hypothesis Testing, Regression Analysis |
| BAIC402 | Design and Analysis of Algorithms | Core | 4 | Algorithm Analysis, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms |
| BAIC403 | Operating Systems | Core | 4 | Process Management, Memory Management, File Systems, I/O Systems, Deadlocks |
| BAIC404 | Computer Organization and Architecture | Core | 3 | Computer Basics, CPU Organization, Memory System, I/O Organization, Pipelining |
| BAIC405 | Theory of Computation | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Pushdown Automata, Turing Machines |
| BAIC406 | Design and Analysis of Algorithms Lab | Lab | 2 | Sorting Algorithms, Searching Algorithms, Graph Traversal, Dynamic Programming Problems, Greedy Algorithm Implementations |
| BAIC407 | Operating Systems Lab | Lab | 1 | Shell Scripting, Process Management, CPU Scheduling, Memory Allocation, File System Calls |
| BAIC408 | Skill Development Course - 2 (Web Technologies) | Skill Development | 1 | HTML5, CSS3, JavaScript Basics, DOM Manipulation, Responsive Design, Web Development Frameworks |
| BIPS409 | Indian Constitution | Mandatory Non-Credit | 0 | Constitutional Framework, Fundamental Rights, Directive Principles, Parliamentary System, Judiciary |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BAIC501 | Software Engineering | Core | 3 | Software Development Life Cycle, Requirements Engineering, Software Design, Testing and Maintenance, Project Management |
| BAIC502 | Computer Networks | Core | 4 | Network Topologies, OSI and TCP/IP Models, Data Link Layer, Network Layer, Transport and Application Layers |
| BAIC503 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Reinforcement Learning, Model Evaluation, Ensemble Methods |
| BAIC504 | Professional Elective - 1 (Choose one from list) | Elective | 3 | Examples: Advanced Java, C# .NET, Advanced DBMS, Data Warehousing, Big Data Analytics |
| BAIC505 | Open Elective - 1 (Choose one from list) | Elective | 3 | Examples: Automotive Engineering, Renewable Energy Sources, Operations Research, Cyber Security |
| BAIC506 | Machine Learning Lab | Lab | 2 | Python for ML, Scikit-learn, Supervised Algorithms, Unsupervised Algorithms, Model Deployment Basics |
| BAIC507 | Computer Networks Lab | Lab | 1 | Network Configuration, Socket Programming, Packet Analysis, Routing Protocols, Network Security Tools |
| BAIC508 | Skill Development Course - 3 (R Programming) | Skill Development | 1 | R Basics, Data Manipulation, Statistical Graphics, Linear Models, Advanced R Programming |
| BHSM509 | Innovation and Design Thinking | Mandatory Non-Credit | 0 | Design Thinking Process, Ideation Techniques, Prototyping, Innovation Strategies, Entrepreneurship |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BAIC601 | Artificial Intelligence | Core | 4 | Problem Solving Agents, Knowledge Representation, Logical Reasoning, Planning, Machine Learning in AI |
| BAIC602 | Deep Learning | Core | 4 | Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, Generative Adversarial Networks |
| BAIC603 | Data Mining | Core | 3 | Data Preprocessing, Association Rule Mining, Classification, Clustering, Outlier Detection |
| BAIC604 | Professional Elective - 2 (Choose one from list) | Elective | 3 | Examples: Image Processing, Natural Language Processing, Robotics, Cyber Physical Systems, IoT |
| BAIC605 | Open Elective - 2 (Choose one from list) | Elective | 3 | Examples: Introduction to Cloud Computing, Entrepreneurship Development, Financial Management, Research Methodology |
| BAIC606 | Artificial Intelligence and Deep Learning Lab | Lab | 2 | AI Search Algorithms, Constraint Satisfaction, TensorFlow/Keras, CNN Implementation, RNN Applications |
| BAIC607 | Data Mining Lab | Lab | 1 | Weka Tool, Data Preprocessing, Association Rule Mining, Classification Algorithms, Clustering Techniques |
| BAIC608 | Skill Development Course - 4 (Android/iOS App Development) | Skill Development | 1 | Mobile App Architecture, UI/UX Design, Android Studio/Xcode, Activity Life Cycle, Database Integration |
| BHSM609 | Universal Human Values | Mandatory Non-Credit | 0 | Self-Exploration, Harmony in Family, Harmony in Society, Harmony in Nature, Professional Ethics |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BAIC701 | Full Stack Development with AI/ML | Core | 4 | Frontend Frameworks, Backend Frameworks, API Development, Database Integration, Deploying AI/ML Models |
| BAIC702 | Big Data Analytics | Core | 3 | Hadoop Ecosystem, Spark Framework, Distributed Computing, NoSQL Databases, Real-time Data Processing |
| BAIC703 | Professional Elective - 3 (Choose one from list) | Elective | 3 | Examples: Quantum Computing, Cognitive Computing, Explainable AI, Generative AI, Computer Vision |
| BAIC704 | Professional Elective - 4 (Choose one from list) | Elective | 3 | Examples: Human Computer Interaction, Distributed Systems, Cloud Computing, Cyber Security for AI, Data Privacy |
| BAIC705 | Project Work Phase - 1 | Project | 4 | Problem Identification, Literature Survey, System Design, Methodology, Initial Implementation |
| BAIC706 | Internship/Industry Project | Internship | 3 | Industry Exposure, Practical Skill Application, Project Documentation, Presentation Skills, Professional Networking |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BAIC801 | Reinforcement Learning | Core | 3 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal Difference Learning, Deep Reinforcement Learning |
| BAIC802 | Optimization for Machine Learning | Core | 3 | Calculus for ML, Convex Optimization, Gradient Descent Methods, Stochastic Optimization, Regularization Techniques |
| BAIC803 | Project Work Phase - 2 | Project | 10 | Advanced Implementation, Testing and Evaluation, Report Writing, Presentation, Demonstration |
