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  – Gamification

Gamification This course provides students with the opportunity to study the impact of gamification in a blended setting. Students will learn basic game theory, explore elements of gaming that can be added to existing courses and to apply the characteristics of a successful gamified course. The end product is a gamified course outline that can be put to use immediately.

Upon completion of this course, students will be able to: Develop a conceptual understanding of game theory, explore some common elements of games, apply gamification strategies to the classroom.

Program Highlights

  • The course presents the application of game elements and digital game design techniques to non-game problems, such as business and social impact challenges.
  • Its main focus is on the mechanisms of gamification, why it has such tremendous potential, and how to use it effectively.
  • In this module, we’ll look at what gamification is, why organizations are applying it, and where it comes from. While there isn’t universal agreement on the scope of the field, a set of concepts are clearly representative of gamification.
  • The course also explains why the concept of games is deeper than most people realize, and how game design serves as a foundation for gamification.

Learning Outcomes

  • Develop a conceptual understanding of game theory
  • Explore some common elements of games
  • Apply gamification strategies to the classroom
  • Design and deliver a gamified lesson from an existing unit of study

Gamification Syllabus

Duration: 70 hrs theory + 30 hrs practical

UNIT I: INTRODUCTION

Introduction – Definition of Gamification – Why Study Gamification – History of Gamification – Game Thinking – Game Elements – Examples and Categories – Gamification in Context – What is a Game – Games and Play – Video Games- Just a Game?

UNIT II: GAME ELEMENTS

Why Gamify- Think like a game designer- Design Rules – Tapping the Emotions – Anatomy of Fun – Finding the Fun – Breaking Games Down – The Pyramid of Elements -The PBL Triad – Limitations of Elements.

UNIT III: THE DESIGN PROCESS AND GAMIFICATION IN PERSPECTIVE

Objectives and Behaviours – Players – Activity Loops – Fun and Tools – Taking Stock – Is Gamification Right for Me – Design for Collective Good – Designing for Happiness.

Pointsification – Exploitation ware – Gaming the Game – Legal issues – Regulatory issues -Beyond the Basics – Inducement Prizes – Virtual Economies – Collective Action – The Future of Gamification.

UNIT IV: GAMIFYING YOUR CLASSROOM

Gains and retains learners’ attention (engages and entertains) – has a competitive narrative – clearly defines policies and procedures – has flow (tasks and rewards are achievable but challenging)- provides fast feedback, and teachers’ learners the content.

Resource exploration:

  • Article: ISTE – “5 Ways to Gamify Your Classroom” (2020)
  • Article: Ditch That Textbook – “20 Ways to Gamify Your Class” (2020)
  • Resource exploration:
  • Article: We Are Teachers – “The Teacher Report: Classroom Management Tricks to
  • Keep Game-Based Learning Running Smoothly” (Hudson, 2012)
  • Video: Tom Driscoll – “Student Perspectives on Gamification” (2013)

UNIT V: GAMIFIED LESSON SUBMISSION AND OBSERVATION

Discussion: Your Gamification Integration Experience – create a video sharing your experience integrating gamification into your BL model. – You may use Screencast-o-Matic, Jing, QuickTime, or any other video making software/digital tool that you prefer. – Provide details regarding one or all of these elements:

  • Student engagement
  • Personalization of the learning experience
  • Student achievement
  • Successes of gamification integration
  • Relevant challenges (and how you overcame them)
  • Anything else you’d like to discuss regarding your blended instructional practice
  • Once you post your video, review and respond to your classmates’ submissions and complete this form.

REFERENCES

  • C BRABHAM, Crowdsourcing, Boston 2013
  • BURKE, gamify: How Gamification Motivates People to Do Extraordinary Things, Gartner 2014;
  • F. HENDRICKS, P.G. HANSEN, Infostorms. How to Take Information Punches and Save Democracy, Springer 2014
  • LERNER, Making Democracy Fun. How Game Design Can Empower Citizens and Transform Politics, Boston (MA) 2014;
  • NORRIS, Digital Divide, Civic Engagement, Information Poverty, Cambridge 2001
  • S. NOVECK, Smart Citizens, Smarter State, Cambridge (MA) 2016
  • R. SUNSTEIN, Why Nudge? The Politics of Libertarian Paternalism, NewHaven 2014