Degree Course from Bangalore University
Artificial intelligence & Cyber Security
Sharing knowledge in computer science to create successful, ethical and effective problem solvers for our society
To provide students with experiential learning opportunities to help them acquire new knowledge to meet the growing demands of a world that keeps changing rapidly
About the Course
The Department of Computer Science at ST PAULS COLLEGE offers professional courses in Computer Application. In this age of modern Technology and digitalization, adequate knowledge about information technology and Applications is indispensable as it helps individuals to have distinct advantages over the others. Bachelor of Computer Applications is a three-year undergraduate programme that focuses on Information Technology and computer applications. The course imparts knowledge about different Computer Applications and how to address and solve problems that arise from various computer applications. The course includes subjects such as core programming languages such as Java, OOPS, Machine Learning, Computer Architecture data structure, Networking and others. BCA provides various opportunities to the students who wish to pursue their career in IT and software. The students gain knowledge on topics like Programming Languages, Hardware and Software, Computer Networks, World Wide Web, Database Management, Software Engineering, etc. A candidate shall be awarded the Bachelor’s Degree in Computer Applications (B.C.A.) after the successful completion of the course that lasts for three years and an Honours in B.C.A. after 4 years.
Software Publishers, Information System Managers, Database Administrators, System Analysts, Chief Information Officers, Computer Graphics, Internet Technologies, Accounting Applications, Personal Information Management, Systems Analysts, Web Developers, Network Administrators, System Managers, Computer Programmers, Software Developers, Software Testers, etc.
- TECHNOVATION – Intra Collegiate IT Fest
- ORACLE – Inter Collegiate ITFest
- TRIATHLON – Intra Departmental Collegiate Fest
- APEIRON – Guest lecture from industry experts
- Industry visits and Internships
- National/International Conferences
- Hands on Skill Development Workshops
- Monthly Seminars And Webinars
- Awareness Programs on Emerging Technologies
- Outreach Activities
- Advanced Computer Lab
- Research Assistance, Paper Presentations, Fests, etc.
- Placement Training – interview skill, group discussion, resume preparation
- Placement Assistance – campus placement drive and campus interview
Specialisations – Artificial Intelligence
Artificial Intelligence (AI) is a research field that studies how to realize the intelligent human behaviours on a computer. The ultimate goal of AI is to make a computer that can learn, plan, and solve problems autonomously. Although AI has been studied for more than half a century, we still cannot make a computer that is as intelligent as a human in all aspects. However, we do have many successful applications. In some cases, the computer equipped with AI technology can be even more intelligent than us. The Deep Blue system which defeated the world chess champion is a well-known example.
To acquire basic knowledge in machine learning techniques and learn to apply the techniques in the area of pattern recognition and data analytics.
At the conclusion of course students are able to:
- Understand to implement the supervised and unsupervised machine learning algorithms.
- Understand the basic principles of machine learning techniques and AI.
Artificial Intelligence Syllabus
Duration: 60 hrs theory + 30 hrs practical + 10 Project/Self-Study
UNIT 1 : INTRODUCTION
Introduction to Artificial Intelligence, AI problems, Applications of AI, Introduction to Natural Language Processing and Expert System, Machine Learning, types of machine learning, examples. Supervised Learning: Learning class from examples, learning multiple classes, regression, model selection and generalization, Parametric Methods: Introduction, maximum likelihood estimation, evaluating estimator, Bayes’ estimator, parametric classification.
UNIT 2: DIMENSIONALITY REDUCTION
Introduction, subset selection, principal component analysis, factor analysis, multidimensional scaling, linear discriminant analysis.
Introduction, mixture densities, k-means clustering, expectation-maximization algorithm, hierarchical clustering, choosing the number of clusters. Non-parametric: introduction, non-parametric density estimation, non-parametric classification.
Introduction, training a perceptron, learning Boolean functions, multilayer perceptron, backpropagation algorithm, training procedures.
UNIT 3: KERNEL MACHINES
Introduction, optical separating hyperplane, v-SVM, kernel tricks, vertical kernel, defining kernel, multiclass kernel machines, one-class kernel machines.
Introduction, estimating the parameter of a distribution, Bayesian estimation, Gaussian processes.
UNIT 4: HIDDEN MARKOV MODELS
Introduction, discrete Markov processes, hidden Markov models, basic problems of HMM, evaluation problem, finding the state sequence, learning model parameters, continuous observations, HMM with inputs, model selection with HMM. Correlation and regression: Linear regression, Rank correlation, Method of least squares Fitting of straight lines and second-degree parabola. Linear regression and correlation analysis
UNIT 5: REINFORCEMENT LEARNING
Introduction, single state case, elements of reinforcement learning, temporal difference learning, generalization, partially observed state.
- Alpaydin, Introduction to Machine Learning. 2nd MIT Press, 2009.
- P. Murphy, Machine Learning: A Probabilistic Perspective. MIT Press, 2012.
- Harrington, Machine Learning in Action. Manning Publications, 2012
- M. Bishop, Pattern Recognition and Machine Learning. Springer, 2011.
- Artificial Intelligence, Elaine Rich, Kevin Knight, Shivashankar B Nair, 3rdedition, McGraw Hill, 2009
Specialisations – Cyber Security
The objective of this training is to provide participants with a comprehensive understanding of cybersecurity, network security, and ethical hacking. The training will also help participants prepare for the CCNA Security and CEH certifications.
Cybersecurity is a critical aspect of modern businesses and organizations. The increasing dependence on technology has made it imperative for individuals and organizations to understand the security risks associated with using the internet and connected devices. The CCNA (Cisco Certified Network Associate) Security and CEH (Certified Ethical Hacker) certifications are widely recognized as the standards for cybersecurity professionals.
Cyber Security Syllabus
MODULE I: INTRODUCTION TO CYBER SECURITY
- Overview of Cybersecurity
- Types of cyber-attacks and threats
- Cybersecurity principles and best practices
- Importance of Cybersecurity
- Overview of CCNA and CEH certifications
Module 2: Network Fundamentals
- Introduction to Computer Networks
- Network protocols and technologies
- Understanding IP Addressing and Subnetting
- Overview of Routing and Switching
- Network Security basics
Module 3: CCNA Security Fundamentals
- Overview of CCNA Security
- Securing Network Devices
- Implementing Access Control Lists (ACLs)
- Implementing Virtual Private Networks (VPNs)
- Introduction to Firewalls
Module 4: CEH Fundamentals
- Overview of Certified Ethical Hacker (CEH)
- Types of Hacking and Types of Hackers
- Overview of Penetration Testing
- Scanning and Enumeration Techniques
- Vulnerability Analysis and Exploitation
Module 5: Threats and Countermeasures
- Overview of Malware and Virus Attacks
- Understanding Spoofing and Sniffing Attacks
- Overview of Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) Attacks
- Countermeasures for various Cyber Threats
- Social Engineering and Phishing Attacks
Module 6: Wireless Security
- Overview of Wireless Networks
- Wireless Security Standards and Protocols
- Wireless Encryption and Authentication
- Overview of Wireless Hacking Techniques
- Countermeasures for Wireless Security Threats
Module 7: Cloud Security
- Overview of Cloud Computing
- Cloud Security Risks and Threats
- Securing Cloud Infrastructure
- Overview of Cloud Encryption and Authentication
- Countermeasures for Cloud Security Threats
Module 8: Cybersecurity Incident Response
- Overview of Cybersecurity Incident Response
- Incident Response Planning
- Detecting and Responding to Cybersecurity Incidents
- Overview of Forensics and Evidence Collection
- Post-Incident Reporting and Lessons Learned
Module 9: CCNA Security and CEH Exam Preparation
- Overview of CCNA Security and CEH Exams
- Exam Format and Content Outline
- Tips for Exam Preparation and Success
- Mock Exams and Practice Tests
- Exam Review and Feedback
Module 10: Conclusion
- Recap of Key Topics Covered
- Summary of CCNA Security and CEH Certifications
- Career Opportunities in Cybersecurity
- Final Thoughts and Recommendations