Class-IX AI Synopsis (Cambridge Court World School)

Maik CAMBRIDGE CoURT wORLD SCHOOL Class- IX-A Synopsis Subject-Artificial Intelligence (417) Unit 1: I...

Maik CAMBRIDGE CoURT wORLD SCHOOL Class- IX-A Synopsis Subject-Artificial Intelligence (417) Unit 1: Introduction to Al: Artificial Intelligence is formed by combining the two wordes Artifcial and Intelligence. Artificial: reiers to something created or produced by humans rather than existing noturaly Intelligence: the ability to acquire and use knowledge and skills is referred to as intelligence. AI Definition:Artificial Intelligence is the ability of machine io do cognitive lasks such as thinking, perceiving, learning, problem solving, and decision making. It /s based on howindividuals use their brains to obserVe, learn, figure out, and make decisions. History of Artificial Intelligence: Al formally founded in 1956. when the term *Artificial Intelligence" was coined at aconference at Dartmouth College in Hanover, New Hampshire. The phrase "Artificial Inteligence" was created by Jolhn McCarthy, who also hosted the first AI conference. 1. 1956- Birth of AIDartmouth Conference 2. 1966- First Chatbot ELIZA" 3. 1972- First Intelligence Robot WABOT-1" 4. 1974-1980:First Alwinter 1980: Expert System 5. 1987-1993: Second AIWinter : Making Things Think 6. 1997- IBM Deep blue First computer to beat a world chess champion 7. 2002- AIin home "Roomba" 8. 2011- IBM Watson "Winsa Quiz show" 9. 2014- Chatbot Eugene Goostman "Wines a Turing test" 10. 2015- Amazon Echo Machine Learning: Machine Learning is a branch of artificial intelligence that allows computers to learn and develop without being explicitly programmed. Machine learning in concerned with the creation of computer program that can access data and learn for themselves. Machine Learning can be used to address difficult problems like detecting credit card fraud, enabling self driving automobiles, and detecting and recognizing faces. Robotics: Robotics is the production of robots that can do activities without the need of human interaction, whereas Alis the process of systems imitating the human mind to make judgments and 'learn'. Applications of Artificial Intelligence 1. AI Application in E-Commerce: Personalized Shoppings Alis used to develop recommendations engines that help you engage with your customers more effectively. These suggestions are based on their previous browsing behaviors, preferences, and interests. Alpowered Assistants; Virtual shopping assistants and chatbots aid in the enhancement of the online buying experience. Fraud Prevention: Two of the most serious difficulties that E-commerce businesses face are credit card fraud and fraudulent reviews. 2. Al in Automobiles: Self-driving cars are built using AI. TO drive the vehicle, AI can be combined with the camera, radar, cloud services, GPS, and control signals. 3. AI in Social Media: Facebook: AI uses the technique known as Deep Text. Deep Text automatically translates the post from one languago toanother language. Twitter: Twitter uses AI for fraud detection, propaganda removal, and to remove hateful content. in the soil 4. AI in Agriculture: With the help of Ai the farmer can identify detects and nutrient deficiencies with the help of machine learning applications. 5. AI in Robotics: Another industry where AI applications are widely used is robotics, Al-powered robots use real-time updates to detect obstructions in their path and instantaneously arrange their route.  Three Domains of AI: There are three domain of AI: L. Data Science (Data for Al): The process of as Data Science. Nowadays Data Scicnce is converting a raw datasct into valuable nowledge is an important part of an industry. Data Science is known domain concerned with data systems and processes, in which the an AI maintains sets of data, andcxtracts meaning from them. system collects a large amount of data, 2. Natural Language Processing (NLP): It is a branch of Al. NLP has the ability to understand text and spoken words in the same manner that humans can. It how humans write and communicate. This is a difficult task focuses on assisting computers in understanding data involved. because of the large amount of unstructured Biome 1uc, 3. Computer Vision (CV): Computer Vision allows computers and from digital photos, vidcos, and other visual inputs, as wellsystems to extract useful information as to conduct actions or make recommendations bascd on that data. The goal of Computer vision is to take necessary action after identifying an objcct or person in a digitalimage. Real World Application: 1. Google Maps and Ride-hailing application: Google Maps is web based service that on geographic regions and locations all over the world. Google has added new features provides accurate data which compare ride services and their pricing with alternative modes of of Google ride transit or walking, in the Google Maps transportation, such as public 2. Face detection (Virtual Fiter, Face D unlocking): Biometrics is used in a facial recognition system to amp facial traits from a photograph or video. To identify a match, it database of known faces. Facial recognition can aid in the verification ofcompares a the information to a person's identification, but it also raises concerns about privacy. 3. Text editors on autocorrect and autocomplete: Autocorrect: This feature corrects any spelling mistakes made while typing. Autocomplete: If a term has already been used, this function provides suggestions for finishing it. If you type ´msword' once ,it will try to complete the sentence by automatically the next line if youtype 'ms'. displaying msword on AI Ethics: AL Ethics are a collectinn of principles thet guide the developnent aad use of artifeial inteltigecâ. Ethical AI can help businesses run more efficiently, provide cleaner impacts, improve publi safety, and improve human health. products, reduce negative environmental Ethical challenges in Artificial Intelligence Cost to innovation Lack of quality data Problems of Integrity Lack of accuracy of data Bias and discrimination Reduction of human contact Loss of human decision making Negative impact on environment  CAMBRIDGE COURT WORLD SCHOOL Class-IX Tashui Synopsis Subject : Artificial Intelligence (417) Unit-2 Data Literacy Data Literacy is the ability to explore, understand, and communicate with data in a meaningful way. A person who can interact with data to understand the world around him can be callcd as Data Literate. Data Pyramid shows the different stages of working with data in a Wisdom bottom to up approach. Why? Data is raw form. It is not very useful. Knowedge Information is the processed data. It has some meaning. How? Information Knowledge is how the information is interpreted. Who? Whar? Wisdom means to take decision based on the knowledge When? Where? acquired. Data Raw Data and information Importance of Data Literacy: Data literacy is essential because it enables individuals to make informed decisions, think critically, solve problenms, and innovate. DataLiteracy Process Framework: Plan Discussio Communicate Assess "Define Goa! Eplain Pupose of the Introduce Participant to " Understand Participants Goa Data Literacy Execution Strategy Commitment towards Assessment Tool Timeline completion of Goal Check Participant comfort level with Data Evaluate Prescriptive Learning Develop Culture " Design an evaluation " Allow learners to make "Improve Data Literacy schedule for program selection of resources Skills through Learning and set itsfrequency based on their learning " Gradually include it in style. current working culture. Data Security and Privacy: DataPrivacy Data Security Definition Data Privacy deals with the proper handling Data security is the practice of of sensitive data and protecting the protecting digital information from confidentiality and inmutability of data. unauthorized accesS, corruption, or theft throughout its entire lifecycle. Compromised Example: Downloading an unverified Any breach can compromise personal by application, Accepting termsof service data, financial infomation, or other without rcading sensitive data such as medical history etc.  Importance The data should be submitted responsibly. Cyber-attacks affect all the people Any manhandling of data can cause breach The fast-technological changes of privacy. will boom cyber attacks Prevention Keep an understanding of collection, Control and protect the transfer of storage and handling of data. sensitive or personal information at Only required data should be collected. every known place. Taking consent of user should be priority while collecting data Cyber Security Practices: Do's: Use strong and different password for each account. Add extra layer of security with two-Factor Software should be downloaded from trusted Authentication.(2FA). source only. Scan all files from external source before Use opening them. secure websites marked with https://" Update OS, Browser and antivirus regularly. Set the visibility for social media accounts to close Lock your desktop when moving away from your contacts only. work desk. Accept requests only from people you know. Use secure Wi-Fi networks. Report any malicious activity such as online bullying, stalking etc. to a trusted adult. Don'ts: Don't share personal information like address, contact details etc. with strangers. Do not post personal pictures on social media platform. Ignore e-mails or attachments from unknown sources. Do not acknowledge messages or requests asking for any Do not share passwords or security questions for any personal or financial information. account with anyone. Copyrighted software should not be copied without permission. Avoid cyberbullying or use of offensive language anywhere on online platforms. Acquiring, Processing and Interpreting Data Types of Data: Continuous Numeric (Quantitative) Discrete Data Textual (Quantitative) Visual Numeric Data: Made of numbers. Used for statistical data. Any measurements, readings or values. Classified as:  Continuous Data is numericdata that is continuous. E.g. height, weight, temperature etc. Discrete Data is numeric data that contains only whole numbers. E.g.: strengthof students, number of items purchased etc. Example: tables, excel sheets, scores, etc. Textual Data: Made of words and phrases Used for NLP (Natural Language Processing). files Example :search queries on search engines, documents, pdf (portable document format) etc. Visual Data: Made of images and videos. Used for computer vision. Acquiring Data or Data Acquisition: DataAcquisition is the process of gathering data. It involves three key steps: (1) (2) (3) Data Augmentation Data Generation Data Discovery Adding more data to the Generate data ifit is not Searchung for new data exstng data Sets available Mikine sight changes to Create new data to Various recordmg 0r Can be explored from detectiung devicescan be Internet ncrease the volunme of used for this purpose, ata Data Sources: Primary Data Source: Data collected through surveys, experiments, interviews, activities etc. This data is called primary data. Secondary Data Source: Data is collected from external data sources. Example of secondary data sources are: Kaggle: an online community of data scientists where different types of data is available..gov datasets: some countries are openly sharing their data sets on various portals. Google Dataset Search: atool provided by google for searching data by name. UCI :acollections of databases, domain theories, data generators in collaboration with University of Massachusetts. Quality of Data: Good Data Bad Data Structure Well Structured Scattered Accuracy Accurate Incorrect value included Consistency Consistent and continuous Contains missing or duplicate values Clarity Clearly presented Poorly presented Relevance Information is relevant to rcquirement Irrelevant information is included  Practice for Acquiring Data: websites: Dain Acquisition from collecting data from websites. O Web Scraping is process of the Various tools can be used. Web scraping is not illegal. should not be used without permission. Data collected through web scraping of data. scraping Make sure that the data source allows occur. issues should be resolved before they Ethical issues in Data Acquisition: These Ethical Issue Prerequisites data. Bias.Tryto avoid any partiality or preferences in using individual Take required permission before collecting or Consent data. Transparency Clearly explain how the collected data will be used. Anonymity Hide the identity of the person who is the source of data. Accountability Take responsibility in case data is misused. Features of Data and its Preprocessing: Usability: Three factors determine the usability of data: Structure: Data should be properly organized under proper headings. E.g. Tabular data is better than data in text format. Cleanliness: Any anomalies such as duplicates, missing values etc. must be removed to maintain reliability and usefulness of data. Accuracy: The data that matches the real world values is considered to be trustworthy and of better quality. Feature of Data is the property of data. It describes each piece of data. E.g numeric data might have some column headings. For visual data the features might be its colours. brightness etc. AImodel requires two types of features: Independent and Dependent. Independent features are the input given to the model. Dependent features are the output or results obtained from the model. Data Processing and Interpretation: Data Processing helps the computer tounderstand raw data and perform operations on it. Data Interpretation is analyzing the data to reach at meaningful decision. Key Terms: Acquire Data: Collecting or gathering raw data Data Processing: Raw data is processed to derive meaningful information. Data Analysis: examine each piece of information to arrive at a conclusion. Data Interpretation: Explain the meaning of conclusions drawn in a given context. Data Presentation: select, organize, and group ideas and evidence in a logical way. Steps for analysis: Qualitative Data: Collcct Data > Oreanise > Set a code on collccted data ’ Analyze data > Report. Quantitative Data: Relate measurenent scalcs with variables > Connect Descriptive Statistics with data ’ decide ameasurement scale > Represent data in an appropriate format. Methods of Data interpretation: Quantitative Data Interpretation Qualitative Data Interpretation Categorical data Numerical data Tells about feelings and emotions of people Speaks about quantity. Answers how, when Answers when how many, how often Methods: Methods: Interviews, Focus Groups Assessments, Tests, Polls, Surveys Data Collection methods: Qualitative Data Interpretation: Record Keeping Observation Case study Focus group Longitudinal studies One-to-one interviews Quantitative Data Interpretation Interviews Polls Observations Longitudinal studies Surveys Data can be presented in three ways: Textual DI: Data is mentioned in the form of text (paragraphs) Used for smallamount of data that can be easily analyzed by reading. Not suitable for huge data. Tabular DI: Data is represented systematically in the form of rows and columns. Title and column heading shows the description of information table holds. Graphical DI: Bar Graph: Data is represented using horizontal or vertical bars. Pie Charts: Data is presented in the form of a pie where each slice represents one category. Line graph: Various datapoints are connected in aline graph. It shows variation in quantity Over time. Importance of Data Interpretation: Informed Decision Making Identify need. Reduced cost.  Topic - Python Fundamentals Getting started with python Tokens Tokens are the least units of programs. These tokens are as follows: 1. Identifiers 2. Keywords 3. Literals 4. Opcrators 5. Punctuators Identifiers Identifiers are names used in prograns to idcntify small units of programs such as variables, objects, classes, functions, etc. Identifiers are defined by the following few rulcs as follows: 1. It must start alphabet 2. It can be a combination of numbers and letters 3. Specialcharacters are not allowed in identifiers name 4. Spaces are not allowed in identifier names, 5. The meaning of Upper Case and Lower Caseunderscore can be used to separate two words different should not usc as identifier names Few Examples: MyData, roll no, yearl, etc. Keywords Keywords are python reserved words used in a progran. Each and every the python interpreter. Ex.: def, False, if, clif, clse, keyword conveys special meaning to for, etc. Literals(Constants) Literals or Constants means that an item(s) have a fixed follows: value. There are several types of constants or literals as 1. String Literals: Ex.: 'a', 'abc', 'my 2. Numeric Literals: int, float, name', t, "n' etc. 3. Boolean Literals: True or Falsecomplex, etc. 4. Special Literals: None Operators Operators are symbols or words used to perform a simple expressions. Python supports these operators: calculation or logical comparisons in statements or 1. Unary Operators: It requires one (Logical Negation) operand Ex.: + (Positive), - (Negative), ~ (Bitwise 2. Binary Operators: It requires more complement), not 3. than one operator. They are as follows: Arithmetic Operators: + (Addition), - (Subtraction), * (Modulus/Remainder), ** (Power), // (Floor division) (Multiplication), /(Division), % 4. Bitwise operator:&& (AND), ^ (XOR), 1 5. Shift Operator: > (Shift (OR) 6. Identity Operators: is, is not Right) (compare 7. RelationalOpcrators: < (less than), > similar identity) equal to), (equal to), != (not (greater than), = (greater than or 8. Logical Operators: and (Logicalequal to) 9. Assignment Operators: =, 5,%,AND), (Logical OR) or 10. Membership Operators: in, not in (use*=,z,%=, **=,l/e to check whether the variable is in sequence or not) These tokens are very important for each program of must understand cach and every aspect of python. So, when you are getting started with Python you them.  CAMBRIDGE COURT wORLD SCHOOL Class-IX Sec- > Synopsis Subject: Artificial Intclligenee Unit-3Maths for ALSttistics and Probability) AI Relation betwecn Math and. Math is the study of patterns.. Al is a way of rccogniz1ng patterns lo make informed decisions.. Al needs help of mathenatical techniques to recognize these palterns. Use of Maths in Al: Statistics(for Data Exploration) and interpreting data. Al uses statistics I is abranch of mathematics that deals with collecting, analyzing, predictions. For example, Al-powered 10 summarize large datasets, identify trends, and make on your past preterences. recommendation systems Use statistics to suggest movies or products based Calculus (Training and Improving Al model): to optimize processes and make This branch deals with rates of change and accumulation. Al uses calculus engines use calculus to rank web incremental improvements over time. For instance, Al-powered search pages based on their relevance to a user's query. Linear Algebra(finding out unknown or missing values): the language of Linear algebra is the study of vectors, matrices, and linear transformations. It's like dimensions (multi-dimensional Al, enabling machines to understand and manipulate data in multiple generate patterns, and even space). For instance, AI uses linear algebra to process images, recognize realistic images from scratch. Probability(predicting different events): make decisions in Probability is the study of uncertainty and randomness. Al uses probability theory to estimate the likelihood of uncertain situations. For example, self-driving cars use probabilistic models to different outcomes, such as whether a pedestrian will cross the road. Statistics Statistics is the technique of collecting, exploring and analyzing data. Conclusions can be drawn from this data. Process: o Data is collected from various sources. o It isexplored and cleaned. o Iis analyzed to get a better understanding. DISASTER o Conclusions and decisions are drawn from data. Application: Disaster Management: Disaster Statistics are used to warn the Management residents of particular location about Preparation Cycle Response any natural calamities in near future. O After fvent Disaster management teams use O Before Event statistics of affected area to make rescue and recovery arrangements. Mitigation Recovery Disaster Managenment Cycle:  Pre-Disaster StepS: Assessment, Miligation, Prepar: ation Post Disaster Steps: Responsc, Recovery Sports: Statistics can be uscd to anily evaluate the perfo)ance of player and tcam. lor tcam and " Helps in making stralegy mpioving game qual1ty. o Prediction ofDiscascs: Statistics hclps to prcdict the impact of a discasc in an arca. Necessary mcasures can be laken lO restric its sprcad and makc arrangcments tocure the discasc. Weather Forccast: Analysis of weather conditions In previous ycar or scasons. Predict the upcoming weather conditions. o Other applications: Preparing reports on various aclivities Analyzing the success of certain programs: e.g. literacy drive Spreading awareness among people. Probability: Probability is the likeliness of occurrence of an event. e.g. The chances of getting a Head(H) or aTail(T) when a coin is tossed Formula: Number of Outcomes favourable to A P(A) = Total Number of Possible Outcomes where, A is an event. P(A)is the probability of occurrence of event A. Example: Find out the probability of getting a Tails when a coin is tossed once. Here, A = Event of getting a Tails in asingle toss. Total Events=2 (either a head(H) or a tail(T)) Favorable Outcome=1 P(A)=1/2 The probability of occurence of an event always lies between 0and Iboth inclusive. Event can be categorized as: o Certain Events willhappen surely. Such events have a probabilityof 1. o Likely event has more probability of occurrence than other event Unlikely event has less chance of occurrence than another event. o Impossible event has no chance of occurrence. These events have a 0 probability. o Equal probability means that each event has equal chance of occurrence. Applicationsof Probability: o Sports Probability is used in sports to predict gameoutcomes, player perlormance, anddecide game strategies. o Weather forecast Meteorologists use probability to predict weather conditions likerain, temperature, and severe weather events. o Traffic Estimation Probability is used intraffic estimation lo predict trafficjams, travel times, and accident locations