NAAC
- Annual Report 2021 – 2022
- Annual Report 2020 – 2021
- Annual Report 2019 – 2020
- Annual Report 2018 – 2019
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.
Job Opportunities
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.
Program Highlights
- 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.
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
