Introduction: what this topic is and why it matters
Artificial Intelligence (AI) is the branch of computer science that builds machines and software able to perform tasks that normally require human intelligence — reasoning, learning, perception, language and decision-making. Machine Learning (ML) is the most successful sub-field of AI: instead of being explicitly programmed with rules, an ML system learns patterns from data. Deep Learning (DL) is a further sub-field of ML that uses many-layered artificial neural networks.
For BCS, Bank and IT-officer examinations this is now one of the highest-yield topics. Examiners ask short "define / difference / example" questions (AI vs ML vs DL, supervised vs unsupervised, classification vs regression), name-the-algorithm items (KNN, k-means, SVM, Naive Bayes, random forest), the neuron/activation-function items, and current-affairs items about ChatGPT and large language models. This lesson covers all of it from first principles so you can answer roughly thirty distinct written questions.
Learning objectives
- Distinguish AI, ML and Deep Learning and place them as nested sets.
- Explain an intelligent agent and describe any task with the PEAS framework.
- Describe an artificial neural network (ANN) using the biological-neuron analogy: dendrites, axon, weights, layers and activation functions (sigmoid, ReLU, tanh).
- Compare supervised, unsupervised and reinforcement learning, and classification vs regression.
- State what each core algorithm does: linear/logistic regression, decision tree, KNN, k-means, SVM, Naive Bayes, random forest and ensembles.
- Diagnose overfitting vs underfitting; apply train/validation/test split, cross-validation and hyperparameters.
- Compute accuracy, precision, recall, F1 and MSE from given data.
- Explain NLP, word embeddings, LLMs/ChatGPT (pros and cons), and outline CNN, RNN, LSTM, GAN, genetic algorithms, federated learning and market-basket analysis.