So I am in 3rd year computer engineering and have a subject AI.
The teacher is not good and hasn’t specified anything and just randomly assigns us work.
I am supposed to do a project on or about AI.
I have to do it alone and also want to learn the subject properly.
Here is my complete AI syllabus. If you have notes, YouTube playlists, cheat sheets, previous papers, or tips for any of these topics, please comment or DM me. Thanks!
Artificial Intelligence (AI) – Complete Syllabus (TE Computer Engineering)
Module 1: Introduction to Artificial Intelligence
Introduction to AI
Definition of AI
History and Evolution of AI
Types of AI (ANI, AGI, ASI)
Applications of AI
Turing Test
Intelligent Agents
Agent and Environment
PEAS Framework
Rational Agents
Performance Measures
Types of Agents
Simple Reflex Agent
Model-Based Agent
Goal-Based Agent
Utility-Based Agent
Environment Types
Real-world Intelligent Agents
Chatbots
Recommendation Systems
Autonomous Systems
Module 2: Problem Solving Techniques
State Space & Search
Problem Solving in AI
State Space Representation
Uninformed Search
Breadth First Search (BFS)
Depth First Search (DFS)
Uniform Cost Search (UCS)
Depth Limited Search (DLS)
Informed Search
Heuristic Search
Best First Search
A* Search
Local Search & Optimization
Hill Climbing
Genetic Algorithms
Adversarial Search
Game Playing
Minimax Algorithm
Alpha-Beta Pruning
Constraint Satisfaction Problems (CSP)
Cryptarithmetic Problems
Sudoku Solver
Self Learning
Bidirectional Search
AO* Search
Module 3: Knowledge Representation & Reasoning
Knowledge Representation (KR)
Logical Agents
Propositional Logic
Inference Rules
First Order Logic (FOL)
Syntax & Semantics
Knowledge Engineering
Forward Chaining
Backward Chaining
Semantic Networks
Ontology
Applications of KR
Retrieval-Augmented Generation (RAG)
Module 4: Fundamentals of Neural Networks
Basics
Biological Neuron
Artificial Neural Networks (ANN)
McCulloch-Pitts Neuron
Neural Network Architecture
Activation Functions
Binary Step
Sigmoid
Tanh
ReLU
Learning Rules
Perceptron
Delta Learning Rule
Backpropagation Algorithm
Supervised Learning
Linear Regression
K-Nearest Neighbours (KNN)
Unsupervised Learning
K-Means Clustering
Apriori Algorithm
Self Learning
Recurrent Neural Networks (RNN)
Convolutional Neural Networks (CNN)
Transfer Learning
Module 5: Associative Memory Networks
Introduction to Associative Memory
Auto-Associative Memory
Hetero-Associative Memory
Bidirectional Associative Memory (BAM)
Hopfield Network
BAM vs Hopfield Network
Applications of Associative Memory
Modern Associative Memory
Agentic AI (Introduction)
Module 6: Modern Trends in AI
Generative AI
Introduction to Generative AI
Large Language Models (LLMs)
Prompt Engineering
Chatbots
AI Content Generation
Ethical Issues in Generative AI
Explainable & Responsible AI
Explainable AI (XAI)
Transparency
Interpretability
Bias
Fairness
Accountability
Responsible AI Frameworks
AI in Emerging Technologies
AIoT (AI + IoT)
Cloud AI
Edge AI
AI in Cybersecurity
AI for Smart Cities
Self Learning
Generative AI Tools (Text, Image & Video)
Future of Artificial General Intelligence (AGI)
Practical/Lab Topics
AI Literature Survey
BFS, DFS, UCS & A* Implementation
Propositional Logic & FOL
Semantic Networks & Ontology
Decision Trees & K-Means
Artificial Neuron & Activation Functions
Feedforward Neural Network using Backpropagation
Copyright & Responsible AI
Prompt Engineering
RAG Chatbot using LLM
Ethical Evaluation of Generative AI
AI Mini Project