23AD02-ARTIFICIAL INTELLIGENCE

 23AD02-AI : Syllabus
 TextBooks:    

1. "Artificial Intelligence :  A Modern Approach" S. Russel    
2. Kevin Night and Elaine Rich, Nair B., “Artificial Intelligence (SIE)”,Mc Graw Hill

• Previous External Papers
• 23AD02-AI- 29th Nov 2025
• 23AD02_AI_8th JUNE 2026

    UNIT-1  and UNIT-2  Notes   
    UNIT-1 :    PPT              Four Definitions of AI       

1. Introduction: AI problems, foundation of AI and history of AI intelligent agents: Agents and Environments, the concept of rationality, the nature of environments, structure of agents, problem solving agents, problem formulation   ppt    PPT2
2. IGNOU Problem Formulation Notes

UNIT - II:

• Searching- Searching for solutions, uniformed search strategies – Breadth first search, depth first Search. Search with partial information (Heuristic search) Hill climbing, A* ,AO* Algorithms, Problem reduction, Game Playing-Adversial search, Games, mini-max algorithm, optimal decisions in multiplayer games, Problem in Game playing, Alpha-Beta pruning, Evaluation functions.
• BFS and DFS  
• Hill Climbing            
• Best  First Search , A* 
• Problem Reduction
• Alpha-Beta Pruning

C Programming - 2026

Text Books 

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•    Let us C by yaswant kantekar              

23CSS1-PYTHON PROGRAMMING

UNIT-5 :Introduction to Data Science: Functional Programming, JSON and XML in Python, NumPy with Python, Pandas.

• NumPy Material

• Pandas Material

• Matplot lib Material

• Experiments --- Programs on Numpy, Pandas, Matplot lib

Role of UML in Software Industry

•  Role of UML in Software Industry

Certifications & FDP (2026)

•  Agentic AI - FDP at ANU, Guntur Certificate

23AM01-Machine Learning

 UNIT-3: 

• Models Based on Decision Trees PPT             Notes
• Decision Trees for Classification, Impurity Measures, Properties, Regression Based on Decision Trees, Bias–Variance Trade-off, Random Forests for Classification and Regression. The Bayes Classifier: Introduction to the Bayes Classifier, Bayes’ Rule and Inference, The Bayes Classifier and its Optimality, Multi-Class Classification, Class Conditional Independence and Naive Bayes Classifier (NBC)            

 UNIT-4:

• Linear Discriminants for Machine Learning:         Notes
• Introduction to Linear Discriminants, Linear Discriminants for Classification, Perceptron Classifier, Perceptron Learning Algorithm, Support Vector Machines, Linearly Non-Separable Case, Non-linear SVM, Kernel Trick, Logistic Regression, Linear Regression, Multi-Layer Perceptron's (MLPs), Backpropagation for Training an MLP. 

 UNIT-5: 

• Fuzzy C Means

23AM01----Machine Learning

Previous Question papers:

• 8th May 2025 - 23AM01_Machine Learning

 Text Books :

1. “Machine Learning Theory and Practice”, M N Murthy,  V S Ananthanarayana, Universities Press (India), 2024.
2. Tom M. Mitchell, “Machine Learning’, MGH, 2017.

UNIT-1

• Introduction to Machine Learning

UNIT-2

• Nearest Neighbor-Based Models