AI Lesson 12 – Probabilistic AI | Dataplexa

AI Course

I. AI Foundations
1. Introduction to AI 2. History of AI 3. AI vs ML vs DL 4. Search Algorithms 5. Intelligent Agents 6. Informed & Uninformed Search 7. Constraint Satisfaction 8. Adversarial Search 9. Logic in AI 10. Planning in AI 11. Knowledge Representation 12. Probabilistic AI 13. Bayesian Networks 14. Markov Models 15. AI Ethics 16. AI Applications 17. Intro to PyTorch 18. Intro to TensorFlow 19. AI Workflows 20. Data for AI
II. Machine Learning
21. Introduction to ML 22. ML Workflow 23. Supervised Learning 24. Unsupervised Learning 25. Reinforcement Learning 26. Linear Regression 27. Logistic Regression 28. SVM 29. Decision Trees 30. Random Forest 31. Gradient Boosting 32. XGBoost 33. K-Means 34. Hierarchical Clustering 35. Dimensionality Reduction 36. Feature Engineering 37. Feature Selection 38. Model Evaluation 39. Overfitting vs Underfitting 40. Cross Validation
III. Deep Learning
41. Intro to Deep Learning 42. Neural Networks 43. Activation Functions 44. Backpropagation 45. Loss & Optimizers 46. Regularization 47. CNN Basics 48. CNN Architectures 49. RNN, LSTM, GRU 50. Seq2Seq 51. Transformers 52. Attention 53. Positional Encoding 54. Training DL 55. Hyperparameter Tuning 56. Transfer Learning 57. Autoencoders 58. GAN Basics 59. GAN Applications 60. Model Serving
IV. Natural Language Processing
61. Intro to NLP 62. Text Processing 63. Tokenization 64. Stopwords & Lemmatization 65. BoW & TF-IDF 66. Word Embeddings 67. Sentence Embeddings 68. NLP Classification 69. Sequence Modeling 70. RNN in NLP 71. Transformers in NLP 72. BERT Basics 73. BERT Fine-tuning 74. RoBERTa & DistilBERT 75. Sentiment Analysis 76. NER 77. Machine Translation 78. Text Generation 79. NLP Evaluation 80. NLP Use Cases
V. Computer Vision
81. Intro to CV 82. Image Processing 83. Filters & Edges 84. Convolutions 85. Object Detection 86. Object Tracking 87. Image Segmentation 88. Face Recognition 89. Feature Detection 90. OCR 91. Image Augmentation 92. Generative Vision 93. Vision Transformers 94. Multimodal Models 95. CV Use Cases
VI. LLM Engineering
96. Intro to LLMs 97. Tokenization 98. LLM Architecture 99. Pretraining 100. Fine-tuning 101. RLHF 102. Prompt Engineering 103. Embedding Models 104. Vector Databases 105. LLM Agents 106. Guardrails & Safety 107. AI in Production 108. End-to-End Project

Probabilistic Artificial Intelligence

In many real-world situations, AI systems do not have complete or perfect information. Sensors can be noisy, data can be missing, and outcomes are often uncertain.

Probabilistic Artificial Intelligence deals with this uncertainty by using probability to represent and reason about uncertain events.

Why Probability Is Needed in AI

Classical logic assumes everything is either true or false. But the real world does not work that way.

  • Medical diagnosis is uncertain
  • Weather prediction is uncertain
  • User behavior is uncertain

Probabilistic AI allows systems to make the best possible decision even when information is incomplete.

Real-World Connection

Consider a spam email filter.

  • An email may look like spam
  • But it might still be legitimate

Instead of saying “spam” or “not spam” with certainty, the system assigns probabilities and decides based on likelihood.

What Is Probability in AI?

Probability measures how likely an event is to happen.

  • 0 means impossible
  • 1 means certain
  • Values in between represent uncertainty

AI systems use probability to model belief rather than absolute truth.

Simple Probabilistic Reasoning Example

Let’s start with a basic probability calculation in Python. 


def probability_of_rain(rainy_days, total_days):
    return rainy_days / total_days

prob = probability_of_rain(30, 100)
print(prob)
0.3

Code Explanation

This function calculates probability using:

  • Number of favorable outcomes
  • Total number of possible outcomes

Here, the probability of rain is 0.3, meaning a 30% chance.

Output Explanation

The output 0.3 tells us rain is possible but not certain. This uncertainty is exactly what probabilistic AI is designed to handle.

Probabilistic Reasoning in AI Decisions

AI systems often compare probabilities before making decisions. 


def decide_action(prob_success):
    if prob_success > 0.7:
        return "Take action"
    else:
        return "Wait"

print(decide_action(0.8))
Take action

Instead of certainty, decisions are based on thresholds and confidence levels.

Where Probabilistic AI Is Used

  • Medical diagnosis systems
  • Spam detection
  • Speech recognition
  • Autonomous vehicles
  • Recommendation systems

Nearly every modern AI system relies on probability at some level.

Practice Questions (With Code Thinking)

Practice 1: What is the probability if an event occurs 50 times out of 100?



Practice 2: If prob_success = 0.9, what will the function return?



Practice 3: Probability in AI is mainly used to handle what?



Quick Quiz (Logic + Code)

Quiz 1: If an event occurs 25 times out of 100, what is its probability?





Quiz 2: AI systems often make decisions based on?





Quiz 3: Which concept allows AI to reason under uncertainty?





Coming up next: Bayesian Networks — structured probabilistic models for reasoning.

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