Computer Fundamentals and Programming Concepts
Covers basic computer architecture, input/output devices, number systems, Boolean logic, algorithms, flowcharts, and programming paradigms including procedural and object-oriented programming. 

Python Programming Basics
Introduction to Python environment, syntax, variables, operators, data types, control structures, loops, strings, collections (lists, tuples, sets, dictionaries), recursion, and basic data structures like arrays, stacks, and queues using Python. 

Linear Algebra and Probability Foundations
Basic operations on matrices and vectors, Euclidean distance, probability distribution, mean, median, variance, and Gaussian distribution—building statistical fundamentals essential for ML. 

Foundations of Artificial Intelligence and Search Algorithms
Historical development of AI, the concept of search as optimization, uninformed (BFS, DFS) and informed (heuristic, greedy, A*, hill-climbing) search methods, and basics of genetic algorithms inspired by Darwin’s theory. 

Knowledge Representation and Reasoning
Propositional logic, truth tables, inference, syllogisms, De Morgan’s laws, fuzzy logic, and unification—understanding how machines represent and reason with knowledge. 

Uncertainty Management in AI
Probabilistic reasoning using Bayes’ theorem, conditional probability, and combining evidence for decision-making under uncertainty. 

Introduction to Chatbots
Basic principles, examples, and flowchart-based mechanisms behind chatbot operation—introducing early applications of natural language interaction. 

Introduction to Machine Learning
Types of learning (supervised, unsupervised, semi-supervised, reinforcement), regression analysis, gradient descent optimization, and concepts of loss functions and training processes. 

Supervised and Unsupervised Learning
Logistic regression, K-nearest neighbors, Naive Bayes, decision trees, K-means clustering, and evaluation metrics (confusion matrix, precision, recall, F-measure) for analyzing model performance. 

Artificial Neural Networks and Ethics in AI
Structure and functioning of artificial neurons, perceptron models, backpropagation, CNNs for image recognition, and discussion on ethical issues such as privacy, bias, and transparency in AI systems.