#AI

Last updated    - V4 (13-Sep-2025)

Level Topic Subtopics Basic Introduction to AI What is AI, History of AI, Applications of AI, Types of AI (Narrow, General, Super AI), AI vs Human Intelligence AI Techniques & Concepts Problem Solving, Search Techniques, Heuristics, Knowledge Representation, Reasoning Machine Learning Basics Supervised Learning, Unsupervised Learning, Reinforcement Learning, Regression, Classification AI Tools & Libraries Python for AI, Numpy, Pandas, Scikit-learn, Matplotlib, TensorFlow Basics AI Ethics & Society AI Ethics, Bias in AI, Responsible AI, AI in Society, Limitations of AI Intermediate Machine Learning Algorithms Decision Trees, Random Forest, K-Nearest Neighbors, Support Vector Machines, Naive Bayes Neural Networks Perceptron, Feedforward Neural Network, Backpropagation, Activation Functions, Gradient Descent Natural Language Processing Text Preprocessing, Tokenization, Lemmatization, Stopwords, Bag-of-Words, TF-IDF Reinforcement Learning Markov Decision Processes, Q-Learning, Policy & Value Functions, Exploration vs Exploitation AI Model Evaluation Confusion Matrix, Accuracy, Precision, Recall, F1 Score, ROC-AUC Advanced Deep Learning Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), LSTM, GRU, Autoencoders Computer Vision Image Classification, Object Detection, Image Segmentation, OpenCV, Pretrained Models Advanced NLP Word Embeddings, Word2Vec, GloVe, Transformer Models, BERT, GPT Optimization Techniques Gradient Descent Variants, Regularization, Dropout, Hyperparameter Tuning, Early Stopping AI Pipelines & Deployment Model Deployment, API Creation, Model Versioning, Monitoring, MLOps Basics Expert Generative AI GANs, Variational Autoencoders, Diffusion Models, Text-to-Image Generation, DeepFakes Advanced Reinforcement Learning Deep Q-Networks, Policy Gradient Methods, Actor-Critic Models, Multi-Agent RL Explainable AI (XAI) SHAP, LIME, Interpretability Techniques, Model Transparency, Trust in AI AI Research & Trends Large Language Models, Self-Supervised Learning, Few-Shot Learning, AI Alignment, Quantum AI AI Ethics & Governance AI Regulation, AI Accountability, Fairness Metrics, Privacy-Preserving AI, AI Risk Management

1. Introduction to AI

1. What is Artificial Intelligence?
2. Differentiate between ANI, AGI, and ASI.
3. List real-world applications of AI.
4. What are the main goals of AI?
5. How does AI differ from traditional programming?
6. What is the Turing Test?
7. Define intelligent agents in AI.
8. Explain rationality in AI systems.
9. What is the role of search algorithms in AI?
10. Define heuristic in AI context.
11. Difference between strong AI and weak AI.
12. What is knowledge representation?
13. Explain ontology in AI.
14. Difference between deterministic and stochastic environments.
15. Explain adversarial search with examples.
16. What is game theory in AI?
17. Explain production systems.
18. Define utility-based agents.
19. What is the frame problem in AI?
20. Explain the concept of AI winter.
21. What is fuzzy logic in AI?
22. Define expert systems with examples.
23. What is the difference between AI and Machine Learning?
24. Explain natural intelligence vs artificial intelligence.
25. What are the challenges in AI adoption?

2. Machine Learning

1. Define machine learning.
2. Types of machine learning: supervised, unsupervised, reinforcement.
3. Difference between classification and regression.
4. What is overfitting?
5. Explain underfitting.
6. What is bias-variance tradeoff?
7. Define cross-validation.
8. Difference between training and testing dataset.
9. Explain confusion matrix.
10. What is precision and recall?
11. Define ROC curve and AUC.
12. Explain clustering with examples.
13. What is dimensionality reduction?
14. PCA vs LDA -- difference.
15. Define feature engineering.
16. Explain ensemble learning.
17. Bagging vs Boosting -- difference.
18. Explain Random Forest algorithm.
19. Explain Support Vector Machines (SVM).
20. Define k-nearest neighbors (KNN).
21. Difference between parametric and non-parametric models.
22. What is gradient descent?
23. Difference between batch and stochastic gradient descent.
24. What is a cost function?
25. Explain reinforcement learning in detail.

3. Deep Learning

1. Define deep learning.
2. Difference between AI, ML, and DL.
3. Explain artificial neural networks.
4. What is backpropagation?
5. Role of activation functions in neural networks.
6. Explain sigmoid, ReLU, and tanh functions.
7. What is dropout in neural networks?
8. Define vanishing and exploding gradient problems.
9. What is gradient clipping?
10. Difference between CNN and RNN.
11. Explain convolution operation in CNNs.
12. What are pooling layers?
13. Explain LSTM architecture.
14. GRU vs LSTM difference.
15. Define attention mechanism.
16. What is transfer learning?
17. Explain fine-tuning in deep learning.
18. What is batch normalization?
19. Define optimizer -- SGD, Adam, RMSProp.
20. Explain cost functions in neural networks.
21. What is autoencoder?
22. Define generative adversarial networks (GANs).
23. Explain diffusion models.
24. What is reinforcement learning with deep networks (Deep Q-Network)?
25. Challenges in deep learning implementation.

4. Natural Language Processing (NLP)

1. What is NLP?
2. Explain tokenization.
3. Difference between stemming and lemmatization.
4. Define POS tagging.
5. Explain n-grams in NLP.
6. What is bag-of-words model?
7. TF-IDF -- explain its use.
8. Define word embeddings.
9. Difference between Word2Vec and GloVe.
10. Explain sequence-to-sequence models.
11. What is machine translation in NLP?
12. Define sentiment analysis.
13. What are named entity recognitions (NER)?
14. Explain text summarization techniques.
15. What is speech recognition?
16. Explain conversational AI and chatbots.
17. Define transformer architecture.
18. What is BERT?
19. What is GPT and how does it work?
20. Explain RNN in NLP context.
21. Difference between CNN and RNN in NLP tasks.
22. What is semantic search?
23. Explain retrieval-augmented generation (RAG).
24. What is prompt engineering?
25. Challenges in NLP.

5. AI Ethics & Safety

1. What is AI bias?
2. Define fairness in AI.
3. Explain transparency in AI models.
4. What is explainable AI?
5. What is SHAP and LIME in interpretability?
6. Define ethical AI principles.
7. What is responsible AI?
8. Explain GDPR's impact on AI.
9. What are risks of AI misuse?
10. Define AI governance.
11. Explain accountability in AI systems.
12. What is adversarial attack in AI?
13. Difference between black-box and white-box attacks.
14. Define robustness in AI systems.
15. What are AI hallucinations in LLMs?
16. Define data privacy in AI.
17. Explain ethical concerns of autonomous vehicles.
18. What is algorithmic discrimination?
19. What is AI safety research?
20. Explain human-in-the-loop in AI systems.
21. What is trustworthy AI?
22. Role of audits in AI ethics.
23. Challenges in AI regulation.
24. What is AI alignment problem?
25. Future scope of responsible AI.

Machine Learning	LLM	GenAI	Deep Learning	NLP