Introduction
Artificial intelligence (AI) is wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. The field of artificial intelligence has been an interdisciplinary endeavor, requiring deep knowledge of both computational and human sciences. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is the science and engineering of making intelligent machines, especially intelligent computer programs. Knowledge Engineering is an essential part of AI research. Machines and programs need to have bountiful information related to the world to often act and react like human beings. AI initiates common sense, problem-solving and analytical reasoning power in machines, which is much difficult and a tedious job. AI and Machine Learning (ML) are the part of computer science that are correlated with each other. These two technologies are the most trending technologies which are used for creating intelligent systems. ML is a subfield of artificial intelligence, which enables machines to learn from past data or experiences without being explicitly programmed.
The major focus of the programme is to equip the students who wish to acquire the ability to design intelligent solutions to real-time problems. This programme discusses artificial intelligence methods based on different fields like neural networks, signal processing and data mining, etc.
Program Overview:
The program begins with introductory courses in programming, computer science, mathematics, and statistics that provide a firm technical foundation. From there, learn core AI concepts and techniques including AI & ML Techniques, Virtual Reality, Web Applications using Machine Learning Techniques, Natural Language and Image Processing, Robotic Process Automation, Business Analytics, Speech Processing, Cognitive systems, Biometrics Systems, computer vision, and language understanding. The program includes a variety of advanced AI electives, enabling technical mastery in specific subfields. Also, specific electives are introduced that will focus on the Application of AI in various industry.
Labs and Practicals:
- Data Structures Lab, Algorithms Lab
- AI and Machine Learning Lab
- Web Applications Lab
- Mobile Application Development & Robotics Lab
- Internet of Things Lab
Career Opportunities
Graduates of the program will be well prepared to work across many sectors, including medicine, finance, robotics, and business intelligence Additionally, you will have a strong technical foundation to pursue graduate work in computer science or AI.
- Software developer
- Artificial intelligence engineer
- Data scientist
Labs and Practicals
- Data Structures Lab, Algorithms Lab
- Micro-controller and Embedded Systems Lab
- AI and Machine Learning Lab
- Web Applications Lab
- Natural Language and Image Processing Lab
- Mobile Application Development & Robotics Lab
- Internet of Things Lab
Department Advisory Board
- Mr. Sunil Kumar, Senior Assistant Professor & HoD, Chairperson
- Dr. Prashanth C M, Principal, Member
- Ms. Radha E G, Assistant Professor, Member
- Ms. Shruthi Kotekar, Adjunct Faculty & Industry Expert, Member
- Dr. Narendra VG, Associate Professor Department of Computer Science & Engineering, MIT, Manipal, Academic Expert
- Ms. Kavyashree Kotekar, Glowtouch Technologies, Mangalore, Industry Expert
Dr. YOGEESHA C.B
Educational Qualification:
- B.E (Computer Science & Engineering)
- M.Tech (Computer Science & Engineering)
- PhD(Computer Science & Engineering)
Total Experience:
- 25 Years 3 Months
Area of Interest:
- Evolutionary Technologies
- Artificial Intelligence (AI)
- Advance Algorithms
- Software Engineering
- Virtualization.
Subjects Handled:
- Data Structures (Basic & Advance)
- Analysis and Design of Algorithms (ADA)
- Software Engineering
- System Software
- Artificial Intelligence
- Advanced Computer Architecture
- Microprocessor.
Professional Membership:
- Life Member of Indian Society of Technical Education (LMISTE) – LM33729
Conference/Journal Publications :
- “An Approach to Design and Implementation of Dynamic Geometric Travelling Salesperson Problem using Hopefield Neural Network Model”, in International Journal of Technology and Engineering System (IJTES) http://www.ijcns.com/pdf/ijtesvol6no1-10.pdf, ISSN:0976-1345, Vol6, No 1, Jan-March 2014, pp 58-64.
- “Highly Scalable Model for Tests Execution in Cloud Environments”, IEEE, 2012 18th International Conference on Advanced Computing and Communications (ADCOM), https://ieeexplore.ieee.org/abstract/document/6563584, 14-16 Dec. 2012
- “Mechanism for on demand Tag-Based software testing in virtualized environments”, IEEE 2012 Fourth International Conference on Advanced Computing (ICoAC), https://ieeexplore.ieee.org/abstract/document/6416808, 13-15 Dec. 2012
- “A Relative Study of Genetic Algorithms and Hopfield Neural Network to Optimize Shortest Path Routing Algorithms for Finding Drilling Holes on the Printed Circuit Board-PCB” Journal of Advanced Research in Dynamical and Control Systems – JARDCS, ISSN 1943-023X, Dec 2017, 15th Special Issue, http://www.jardcs.org/abstract.php?archiveid=2187, pp 801-808
- “Solution to Solve Travelling Spider Problem (TSpP) Using nXn Hopfield Network” Journal of Advanced Research in Dynamical and Control Systems – JARDCS, ISSN 1943-023X, Dec 2017, 15th Special Issue, http://www.jardcs.org/abstract.php?archiveid=2186, pp 793-800.
- Randomized Algorithms: On the Improvement of Searching Techniques Using Probabilistic Data Structure Linear Linked Skip Lists, Proceeding of ICAdC, AISC, springerlink, http://link.springer.com/chapter/10.1007/978-81-322-0740-5_19, pp 147-153, 2012
- Enhanced Relative Comparison of Traditional Sorting Approaches towards Optimization of New Hybrid Two-in-One (OHTO) Novel Sorting Technique, Journal of ICT Research and Applications, 17(2), 151-166. https://doi.org/10.5614/itbj.ict.res.appl.2023 .1 7.2.2, 2023
Dr. YOGEESHA C.B
Dean (Industry Collaborations) & Professor
Dr. Farha Haneef
Educational qualification
• BE-Computer science and Engineering
• M.Tech- Computer Science and Engineering
• Ph. D- Computer Science and Engineering
Total experience
24 years
Google scholar ID: https://scholar.google.com/citations?user=j_HwYsIAAAAJ&hl=en
Orcid ID: https://orcid.org/0000-0002-7320-1394
Areas of Interest
• Machine Learning
• Cyber Security
• Artificial Intelligence
Journal and conference Publications
1. Asthana, S., Haneef, F., & Bhujade, R.K. (2011). Handwritten multiscript numeral recognition using artificial neural networks. International Journal of Soft Computing and Engineering, 1(1), 1–5.
2. Kushwaha, G.R., & Haneef, F. (2011). A Novel RSRM Algorithm for Mining Services for Better Enhancement in Small Hand Held Devices. International Journal of Computer Applications, 975, 88–87.
3. Haneef, F., Kushwaha, G.R., & Dubey, A.K. (2011, June). Analysis with data mining and ant colony algorithm for implementing of object pool optimization. In 2011 International Conference on Communication Systems and Network Technologies (pp. 313–317). IEEE.
4. Haneef, F., & Singh, S. (2017). A Feature Selection Technique for Intrusion Detection System based on IWD and ACO. International Journal of Advanced Research in Computer Science, 8(9).
5. Haneef, F., & Singh, S. (2017). Improved intrusion detection system based on optimized SVM using M-FOA. International Journal of Advanced Research and Development, 2(2), 644–650.
6. Singh, S., Haneef, F., Kumar, S., & Ongsakul, V. (2020). A framework for successful IoT adoption in agriculture sector: a total interpretive structural modelling approach. Journal for Global Business Advancement, 13(3), 382–403.
7. Agrawal, N., & Haneef, F. (2020). A Review on Image Object Segmentation Features on Various Techniques and Limitations. International Journal of Scientific Research & Engineering Trends, 6(5), Sept–Oct 2020.
8. Walke, P.P., & Haneef, F. (2021). A Survey on “Machine Translation Approaches for Indian Languages”. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(3), 4792–4794.
9. Pathak, K.R., & Haneef, F. (2022). Human Stress and Pressure Detection Using a Machine Learning Approach with EEG Signal. Webology, 19(3).
10. Nandwalkar, B.R., & Haneef, F. (2023). Data Embeddings in Medical Applications: A Survey of Techniques and Applications. International Journal of Intelligent Systems and Applications in Engineering, 11(7s), 561–579.
11. Walke, P.P., & Haneef, F. (2024). BMIRTE: Design of a Bio-inspired Model for Improving Readability of Translated Sentences via Ensemble Operations. International Journal of Intelligent Systems and Applications in Engineering, 12(17s), 527–535.
12. Walke, P.P., & Haneef, F. (2024). TMIANE: Design of an Efficient Transformer-Based Model for Identification & Feature Analysis of Named Entities via Ensemble Operations. J. Electrical Systems, 20-1s, 338–352.
13. Nandwalkar, B.R., & Haneef, F. (2024). Harnessing the Power of Multimodal Data: Medical Fusion and Classification. Advances in Nonlinear Variational Inequalities, 27(1). https://doi.org/10.52783/anvi.v27.318
14. Nandwalkar, B.R., & Haneef, F. (2025). Integrative Multimodal Data Fusion for Medical Diagnostics: A Comprehensive Methodology. Journal of Information Systems Engineering and Management, 10(2).
15. Agrawal, D.R., & Haneef, F. (2025). Eye Blinking Feature Processing Using Convolutional Generative Adversarial Network for Deep Fake Video Detection. Transactions on Emerging Telecommunications Technologies, 36(3). https://doi.org/10.1002/ett.70083
Dr. Farha Haneef
Associate Professor
Dr. M.Amirthavalli
Educational Qualification: M.E (CS&E), Ph.D
Total Experience:7
Area of Interest: Cryptography and Network Security, Machine Learning, Secure Programming
Subjects Handled: Computer Networks, Python Programming, Unix and Network Programming, Secure Programming, Java.
Dr. M.Amirthavalli
Assistant Professor
Mr. Srivatsa Upadhya P
Education Qualification:
- BTech (CSE)
- MTech (SE)
Total Experience: 1year 3months
Area of Interest: Log Analysis, Data Obfuscation, Linux, Python, ELK Stack, Machine Learning.
Mr. Srivatsa Upadhya P
Assistant Professor
Infrastructure
All the laboratories are well designed and equipped with latest computer systems. All systems are interconnected through LAN having sophisticated servers and internet connectivity with adequate bandwidth. All the laboratories have requisite software for undertaking all kinds of latest projects.
Major Labs
Algorithms Lab/ Artificial Intelligence Lab
The lab is equipped with 32 personal computers with latest configuration. PCs have windows 11 operating system with Java, Eclipse, Python and latest browsers. Students carry out programs related to Algorithms in Eclipse and Java and Artificial Intelligence practical in Python.
Data Structures/ DBMS lab
The lab is equipped with 32 personal computers with latest configuration. PCs are installed with Ubuntu operating system, C/C++, Java, MySQL and latest internet browser. Students perform practical related to solving data structure problems. Students perform database related practical and develop mini project in this lab.
Department Forum – ARTIFERA
ARTIFERA – ARTIFICIAL INTELLIGENCE ERA is a student association of Artificial Intelligence & Machine learning department established on 17 th November, 2021. Its main objective is to create awareness and groom professionals skilled in the area of Artificial Intelligence & Machine learning. All students of Artificial Intelligence and Machine learning department are members of this association. It provides platform to the students to enhance their technical as well as non technical skills. ARTIFERA organizes various technical events like technical talks, workshops, quiz, project competition and exhibitions etc. In addition, department forum aims to promote research and entrepreneurial skills in students. Students are encouraged to participate in non technical events also for their holistic development.
“Our intelligence is what makes us human, and AI is an extension of that quality.” – Yann LeCun
Committee Members – Academic Year 2024-25
1. President: Mr. Amogha Acharya , 4th Year AIML
2. Vice President:
oMr. Abdul Rehman , 3rd Year AIML
oMs. Nandini Jagadish Hegde, 3rd Year CSE(AIML)
3. Secretary: Mr. Manoj N, 4th Year AIML
4. Joint Secretary: Ms. Nandini V C, 2nd Year AIML
5. Treasurer: Ms. Jenisha Shereyl Dsouza, 2nd Year CSE(AIML)
6. Sports Coordinator: Mr. Sujal Revankar, 3rd Year CSE(AIML)
7. Cultural Coordinator: Ms. Hitha B Mendon, 4th Year AIML
Activity Report
Academic Year 2023-24 Click Here
Study Materials
Syllabus
2022 Scheme- Click Here
2023 – II Year Scheme & Syllabus (Autonomous)- Click Here
2023 – III Year Scheme & Syllabus (Autonomous)- Click Here
Model Question Paper (Autonomous)
Third Semester
| Engineering Mathematics- III | Click Here |
| Data Structures and Applications (23CSPC203) | Click Here |
| Digital System Design (23CSPC204) | Click Here |
| Computer Organization (23SCPC205) | Click Here |
| Software Engineering (23CSPC206) | Click Here |
| Universal Human Values (23HMCC215) | Click Here |
Fifth Semester
| Entrepreneurship, Management & Finance (23HMCC301) | Click Here |
| Database Management Systems (23CIPC302) | Click Here |
| Computer Networks (23CIPC303) | Click Here |
| Mathematics for Artificial Intelligence (23CIPC304) | Click Here |
| Full Stack Development (23CIPE311) | Click Here |
COURSE MATERIALS
AUTONOMOUS Scheme
OPEN ELECTIVE COURSE HAND BOOK FOR 6th SEM – 2026
| Course Code | Course Name | Couse Hand Book | Model Question Paper |
| 23AIOE322 | Introduction to Machine Learning | Click Here | Click Here |
5th Semester
| Entrepreneurship, Management Finance [23HMCC301] | Module 1 | Module 2 | Module 3 | Module 4 | Module 5 |
| Database management system [23CIPC302] | Module 1 | Module 2 | Module 3 | Module 4 | Module 5 |
| Computer Networks [23CIPC303] | Module 1 | Module 2 | Module 3 | Module 4 | Module 5 |
| Mathematics for Artificial Intelligence [23CIPC304] | Module 1 | Module 2 | Module 3 | Module 4 | Module 5 |
| Full stack development [23CIPE311] | Module 1 | Module 2 | Module 3 | Module 4 | Module 5 |
VTU 22 Scheme
7th Semester
| Deep Learning [BCA701] | Module 1 | Module 2 | Module 3 | Module 4 | Module 5 |
| Machine Learning -II [BAI702] | Module 1 | Module 2 | Module 3 | Module 4 | Module 5 |
| Cryptography & Network Security [BCS703] | Module 1 | Module 2 | Module 3 | Module 4 | Module 5 |
| Data Engineering & MLOps [BAD714C] | Module 1 | Module 2 | Module 3 | Module 4 | Module 5 |