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CBSE DEPARTMENT OF SKILL EDUCATIONCURRICULUM FOR SESSION 2021-2022ARTIFICIAL INTELLIGENCE (SUB. CODE 843)CLASS – XI & XIICOURSE OVERVIEW:AI is a discipline in computer science that focuses on developing intelligent machines, machines thatcan learn and then teach themselves. These machines, then, can process vast amounts of data thanhumans can, and several times faster. However, AI can go across all disciplines to change the worldfor the better– from creating new healthcare solutions, to designing hospitals of the future, improvingfarming and our food supply, helping refugees acclimate to new environments, improving educationalresources and access, and even cleaning our oceans, air, and water supply. The potential for humansto improve the world through AI is endless, as long as we know how to use it.OBJECTIVES OF THE COURSE:In this course, the students will develop knowledge, skills and values to understand AI and itsimplications for our society and the world and to use AI to solve authentic problems, now and in thefuture. The students will engage with a host of multi-media online resources, as well as hands-onactivities and sequence of learning experiences.The following are the main objections of the course:1.Develop informed citizens with an understanding of AI and the skills to think critically andknowledgeably about the implications of AI for society and the world2.Develop engaged citizens with a rigorous understanding of how AI can be harnessed toimprove life and the world we live in3.Stimulate interest and prepare students for further study to take up careers as AI scientistsand developers to solve complex real world problemsSCHEME OF UNITSThis course is a planned sequence of instructions consisting of units meant for developingemployability and vocational competencies of students of Class XI opting for skill subject along withother education subjects. The unit-wise distribution of hours and marks for class XI is as follows:843 – AI - Class XI & XII – 2021-2022Page 1 of 12

ARTIFICIAL INTELLIGENCE (SUBJECT CODE - 843)Class XI (Session 2021-22)Total Marks: 100 (Theory - 50 Practical - 50)TermUNITSHOURSMAX. MARKS(Theory Practical)(Theory Practical)Employability SkillsPart ATerm ITerm IIUnit 1 : Communication Skills-IIIUnit 2 : Self-Management Skills-III1010Unit 3 : ICT Skills-III10Unit 4 : Entrepreneurial Skills-III15Unit 5 : Green Skills-III05Total501010Subject Specific SkillsPart DPart CPart BTerm ITerm IIUnit 1: Introduction To AI30Unit 2: AI Applications & Methodologies*Unit 3: Maths For AI3010Unit 4: AI Values (Ethical Decision Making)5Unit 5: Introduction To Storytelling*20Unit 6: Critical & Creative Thinking*Unit 7: Data Analysis (ComputationalThinking)*Unit 8: Regression530Unit 9: Classification & Clustering20Unit 10: AI Values (Bias Awareness)**Unit 2, 5, 6, 7 & 10 are to be Assessedthrough Practicals OnlyTotalPractical Work – Unit 2: AI Applications & Methodologies Unit 5: Introduction To Storytelling Unit 6: Critical & Creative Thinking Unit 7: Data Analysis (ComputationalThinking) Unit 10: AI Values (Bias Awareness)Practical ExaminationViva-VoceTotal30302102020404040Project Work/ Field Visit/ Project/Ideation presentation10Viva-VoceTotalGRAND TOTAL843 – AI - Class XI & XII – 2021-202210260100Page 2 of 12

DETAILED CURRICULUM/ TOPICS FOR CLASS XIPART-A: EMPLOYABILITY SKILLSS. No.UnitsDuration in Hours1.Unit 1: Communication Skills-III102.Unit 2: Self-management Skills-III103.Unit 3: Information and Communication Technology Skills-III104.Unit 4: Entrepreneurial Skills-III155.Unit 5: Green Skills-III05TOTALNOTE:50Detailed Curriculum/ Topics to be covered under Part A: EmployabilitySkills can be downloaded from CBSE website.Part-B – SUBJECT SPECIFIC SKILLS TERM I:Level I: AI Informed(AI Foundations) Unit1:Introduction to AI Unit 2:AI Applications & Methodologies* Unit 3:Math for AI Unit 4:AI Values (Ethical Decision Making) Unit 5:Introduction to Storytelling* Unit 6:Critical & Creative Thinking* Unit 7:Data Analysis (Computational Thinking)* Unit 8:Regression Unit 9:Classification & Clustering Unit 10:AI Values (Bias Awareness)* TERM II:Level 2: AI Inquired(AI Apply)NOTE: * UNITS 2, 5, 6, 7 & 10 should be assessed in Practical Examinationonly and should not be assessed in Theory Examination.843 – AI - Class XI & XII – 2021-2022Page 3 of 12

DETAILED CURRICULUM/ TOPICSLEVEL I: AI INFORMED (AI Foundations) UNITUnit 1:Introduction(knowledge)Unit 2: AIApplicationsandMethodologiesTOPICSLEARNING OUTCOMESIntroduction-AI for everyone What is AI?o Kids can AI History of AI What is Machine Learningo Difference between conventionalprogramming and machine learningo How is Machine learning related toAI? What is data?o Structuredo Unstructuredo Examples of unstructured data- text,images Terminology and Related Concepts Intro toAIo Machine learningo Supervised learning (examples)o Unsupervised learning (examples)o Deep learningo Reinforcement learningo Machine Learning Techniques andTrainingo Neural Networks What machine learning can and cannot do More examples of what machine learningcan and cannot do Jobs in AIPresent day AI and Applications (Introduction)(Knowledge) Key Fields of Application in AIo Chatbots (Natural LanguageProcessing, speech)o Alexa, Siri and otherso Computer visiono Weather Predictionso Price forecast for commoditieso Self-driving carsCharacteristics and types of AIo Data driveno Autonomous systemso Recommender systemso Human like843 – AI - Class XI & XII – 2021-2022Knowledge – Define AI andMLComprehension – What arethe AI products/ applicationsin society and how are theydifferent from non-AIproducts/ applications?Evaluation – What kind ofjobs may appear in thefuture?Knowledge – Where can AIbe applied (like in the field ofComputer vision, Speech,Text, etc.), What is deeplearning?Comprehension – How AIwill impact our societyAnalysis – How should weget ready for the AI age(future)Page 4 of 12

UNITTOPICS Unit 3:Maths for AI (Recap) (Knowledge) Unit 4: AIValues(Ethicaldecisionmaking)(Values)Unit 5:Introductionto storytelling(Skills)Cognitive Computing (Perception, Learning,Reasoning) Cognitive computingRecommended deep-dive in NLP, CV, etc.*AI and Society coursera-ai-for-everyoneThe Future with AI, and AI in Action(Introduction)Non-technical explanation of deep learningcoursera-ai-for-everyoneIntroduction to matrices (Recap)Introduction to set theory (Recap)o Introduction to data table joinsSimple statistical conceptsVisual representation of data, bar graph,histogram, frequency bins, scatter plots, etc.With co-ordinates and graphs introduction todimensionality of dataSimple linear equationo Least square method of regressionAI: Issues, Concerns and Ethical Considerations Issues and Concerns around AIAI and Ethical ConcernsAI and BiasAI: Ethics, Bias, and TrustEmployment and AIStorytelling: communication across the ages Learn why storytelling is so powerful andcross-cultural, and what this means fordata storytellingThe Need for StorytellingStory telling with data By the numbers: How to tell a great storywith your data.Conflict and Resolution Everyone wants to resolve conflict, and agood data storyteller is there to help!Storytelling for audience Your data storytelling depends on thebackground knowledge of your audience.Insights from storytelling Make the audience care about the data Keep the audience engaged Create from the end; present from thebeginning Start with an anecdote, end with the data Build suspense, not surprise843 – AI - Class XI & XII – 2021-2022LEARNING OUTCOMESComprehension – LinearAlgebra, Statistics, Basics ofGraphs and Set theoryApplication – Application ofMath in AISynthesis – Representingdata in term of mathematicalformulaKnowledge – Ethics, Bias,Impacts of bias on societyApplication – Spot issue indata, Make arguments, ApplyrulesSkill – Imagination, mappingthe plot into key eventsincreasing memory retention.Application- Helping increating blogs, videos, andother content.Page 5 of 12

LEVEL 2: AI INQUIRED (AI Apply)UNITUnit 6: Criticaland CreativethinkingTOPICS Design thinking frameworko Right questioning (5W and 1H)o Identifying the problem to solveo Ideate Types of structured datao Date and timeo Stringo CategoricalRepresentation of dataExploring Data Exploring data (Patternrecognition)o Cases, variables and levels ofmeasuremento Data matrix and frequency tableo Graphs and shapes of distributionso Mode, median and meano Range, interquartile range and boxplot*o Variance and standard deviation*o Z-scores*o Exampleo Practice exerciseCorrelation and Regression Crosstabs and scatterplots Pearson's r Regression - Finding the line Regression - Describing the line Regression - How good is the line? Correlation is not causation Example contingency table Example Pearson's r and regressionReadings(Skills)Unit 7: DataAnalysis(Computationalthinking)(Skills)Unit 8:Regression(Knowledge) LEARNING OUTCOMESSkill – Understanding the problemand being able to express thesameCreativity – To be able todevelop/innovate from design asolutionKnowledge – Types of structureddata, statistical principals –frequency tables, mean, median,mode, range, etc.Application – Representing datain terms of graphs, statisticalmodelsSynthesis – To be able torepresent a simple problem interms of numbersKnowledge – Correlations,Regression, and other relatedtermsApplications – Being able torelate data with regression andcorrelation. Everyday applicationsof these mathematical concepts. CorrelationRegressionCaveats and examplesPractice exercise Correlation andRegression Explain the importance of data fromabove examples How prediction changes withchanging data?843 – AI - Class XI & XII – 2021-2022Page 6 of 12

Unit 9:Classification& Clustering What is a classification problem?Examples- Simple binary classification(Knowledge) Introduction to binary classification withlogistic regressionTrue positives, true negatives, falsepositives and false negatives Where we should care morewith examples Example- false negative of adisease detection can havedifferent implication than falsepositive, one will be morephysical harm and other will bementalPractice exercise on simple BinaryClassification modelWhat is a clustering problem?Why is it unsupervised?ExamplesPractice exercise on simpleClustering model Unit 10: AIValues (Biasawareness) (Values) AI working for goodPrinciples for ethical AITypes of bias (personal /cultural/societal)How bias influences AI baseddecisionsHow data driven decisions can be debiasedHands on exercise to Detect theBias (Intro to AI)Knowledge – What isclassification and its types, whatkind of problems may be placedunder the category of aclassification problemApplications – Where to applyclassification principalsAnalysis – Impact of theapplication of incorrect algorithmson societyKnowledge – Clustering problemsand its application, why is it calledclusteringApplication – Application ofclustering problem using standardmodelsKnowledge – What is ethics,Impact of ethics on society, theimpact of bias on AI functioningEvaluation – Biases in data, howto de-bias or neutralize the biaseddataApplication – Finding bias inacquired datasetNOTE: UNITS 2, 5, 6, 7 & 10 should be assessed through Practicals only and shouldnot be assessed with the Theory Exam.843 – AI - Class XI & XII – 2021-2022Page 7 of 12