This course covers the fundamental concepts of probability theory, including sample space, events, and the axioms of probability. It explores both the Bayesian and frequentist viewpoints, conditional probabilities, independence, and introduces the two-state Markov chain. Additionally, the course discusses Bayes' theorem, naïve Bayes, classical probability distributions (both discrete and continuous), and key concepts such as expected value and variance. The course offers a solid foundation in probability theory with real-world applications.  

• Sample space and events 
• Basic axioms of probability theory 
• Bayesian vs frequentist: comparison of the two approaches. 
• Conditional probability and independence. Basics of two-state Markov chains. 
• Bayes' theorem: Updating probabilities with new information. 
• Introduction to naïve Bayes classifier. 
• Classical distributions: discrete and continuous. 
• Expected value, variance, and standard deviation.

• Understand key probability concepts, including sample space and events. 
• Apply probability axioms and compare Bayesian and frequentist approaches. 
• Calculate conditional probabilities and recognize independence. 
• Use Bayes' Theorem for probability updates. 
• Apply Naïve Bayes for classification tasks.
•  Work with classical probability distributions.
• Calculate expected value and variance.

The course follows a traditional classroom format with slide presentations, fostering interactive engagement and active participation.