Page 10 - Touhpad Ai

PAPER I: THEORY—70 MARKS

1. Basic concepts of Artificial Intelligence
(i) Artificial Intelligence
Definition, Evolution, Applications in different fields, commonly used AI applications, benefits – decision making,
remote patient monitoring, analysis of data, solving complex problems, etc.
(ii) Role of data and information, evolution computing

Types of data, identification, acquiring and exploring the data, binary logic system, conditional gates, deterministic
and probabilistic nature of real- life problems with appropriate examples.

(iii) Overview of Decision making
Decision making in machines/computers; Cyber security in computing and machine intelligence.
(iv) Components of AI project framework
Problem scoping, data acquisition, data exploration, modeling and evaluation (in brief)
(v) Overview of Data representation and programming in Python
Datatypes, variables, operators, conditional statements, control statements, functions.

2. Introduction and State of Art of AI, Natural Language Processing (NLP), and Potential use of AI
(i) Brief History and Primary elements of AI
Definition of Machine Learning (ML) and Deep Learning (DL)/Neural Networks. Application of ML and DL: Image
recognition/processing and Computer vision, Speech recognition, Information Retrieval (IR) through Search Engine, etc.

(ii) Domain of Natural Language Processing (NLP)
Text understanding, Text generation, Language translation (e.g., Google translate), Question answering (Chatbots),
Dialogue systems (e.g., Siri and Alexa),Internet searches such as navigation searches (related to familiar brands
and platforms, e.g., LinkedIn, YouTube), informational searches (for learning and understanding), transactional
searches (for purchasing, signing up for services, or downloading apps), investigative searches (e.g., top-rated
web series, movies), and voice searches.
(iii) Potential use of AI
Use of AI in various domains in Word Processing System like Smart Phones, Webbased Auction sites, Scanner
machines, ecommerce platforms and social networking sites (brief explanation).
(iv) AI and Society

Social benefits of AI: Healthcare (enhancement in diagnosis treatment plans and patient care), Transportation
(Autonomous vehicles and transport management system), Disaster Prediction (Early warning system and
response management) and Agriculture (Precision farming, crop monitoring and yield prediction).

3. Mathematics for AI
(i) Matrices
Introduction to Matrices, Types of Matrices, Matrix Operations (Addition, subtraction, multiplication, transpose).
(ii) Vectors and its applications

Vector arithmetic
(iii) Set Theory
Introduction to data table joins, Context setting, Set Theory and Relational Algebra, Set operations.
(iv) Simple Statistical Concepts
Measures of Central Tendency (Mean, Median, Mode), Variance and Standard Deviation.