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BSC

Degree Course from Bangalore University

Artificial intelligence & Gamification

Vision

Sharing knowledge in computer science and psychology to create successful, ethical and effective problem solvers for our society

Mission

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

Computer Science

Our college offers courses in computer science at an undergraduate level to respond to the rising demand in this field owing to the rapid growth of IT and software industries in the country. A career in Computer Science has been proved lucrative and rewarding since last decade. Students of computer science are trained not only in the use of various software but also have the opportunity to acquire knowledge of operating systems, programming language, data base, etc. With the opening of many software and IT companies in India, the job opportunities for trained professionals have increased considerably. 

Psychology

The course focuses on the scientific study of the human mind and how it dictates and influences our behavior from communication and memory to thought and emotion. The objective is to understand what makes people think and how this understanding can help them address many of the problems and issues in society today. The course focuses on the study of human behavior and the thoughts, feelings, and motivations behind it through observation, measurement and testing in order to form conclusions that are based on sound scientific methodology.

Job Opportunities

Psychologist, Psychiatrist, Counsellor, Therapist, Lecturer, NGO, Programme Developer and Analyst, Artist, Designer, Engineer, Systems Manager, IT Professional, Public Administrator, Researcher Scholar, Public Relations Officer, etc.

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Program Highlights

  • IKANOS – Intra Departmental Humanities & Science Fest (UG & PG)
  • HUMANZA – Inter Collegiate Humanities & Science Fest
  • MUGSHOT – National Level Photography Exhibition
  • BONSAI – National Level Short Film Festival
  • MEDIA BASH – PU/12th Media Fest and Panel Discussion
  • Seminar and workshop series
    • Journalism: TATTVA MANDALA – Where thought and wisdom converge
    • Psychology: YĀNA – a journey towards a better life
    • English: LEND ME YOUR EARS
    • Political Science: LYCEUM
    • Languages: PRABUDH CHARCHA
  • PAULA PATRIKA – In house fortnightly newsletter
  • COSMOS – In-house half yearly magazine
  • DRISHTI – YouTube channel for practical learning
  • ST PAULS TV – In house television channel for practical learning
  • Visit to NIMHANS brain museum
  •  Well-equipped psychology, journalism and computer labs
  • Industry visits and Internships
  • Creative and enriching Outreach activities
  • Interaction with authors, poets, psychologists, journalists and industry experts
  • Coaching for Competitive Exams
  • Advanced Edit Lab and Media 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.

Program Objectives

  • To acquire basic knowledge in machine learning techniques and learn to apply the techniques in the area of pattern recognition and data analytics.

Learning Outcome

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.

CLUSTERING

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.

MULTILAYER PERCEPTRON

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.

BAYESIAN ESTIMATION

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.

 

REFERENCES

  • 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.

Program Highlights

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
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