đź“š Probability and Statistics Course

Course Title: Probability and Statistics for Computer Science

Target Audience :

  • Level: Second-Year Undergraduate Students (L2) in Computer Science and Computer Engineering.

  • Instructor: Dr. AINOUCHE Amina

  • Department: Faculty of Computer Science, USTHB

Course Objectives:

This module equips second-year computer science students with a rigorous mathematical foundation in combinatorial analysis, probability theory, and statistical modeling applied directly to computing systems:

  1. Master Core Counting & Probability Concepts: Develop a thorough understanding of search space enumeration, conditional probability, Bayes' theorem, and probability distributions.

  2. Model Computing Systems: Learn to translate computer science scenarios—such as network traffic, packet loss, server loads, execution latency, and hardware failure rates—into structured probabilistic models.

  3. Evaluate System Performance: Apply statistical metrics (expected value, variance, standard deviation) to measure average performance, stability, and resource allocation efficiency.

  4. Prepare for Advanced Fields: Establish the mathematical toolkit essential for machine learning, data analytics, cryptography, cybersecurity, queueing theory, and algorithm analysis.

Targeted Competencies (Learning Outcomes):

By the end of this course, students will be able to:

  • Combinatorial Analysis & Complexity: Calculate search spaces, system state complexities, and brute-force key limits using permutations, combinations, and counting principles.

  • Probabilistic Diagnostic Reasoning: Apply conditional probability and Bayes' Theorem to solve diagnostic problems like spam filtering and network fault identification.

  • Discrete Phenomena Modeling: Formulate Probability Mass Functions (PMF) and Cumulative Distribution Functions (CDF) using Binomial, Poisson, and Geometric distributions to evaluate packet delivery and server request traffic.

  • Continuous System Analysis: Utilize Probability Density Functions (PDF) and continuous distributions (Exponential, Normal, Uniform) to evaluate hardware lifespans, latency, and memory allocation.

  • Performance Assessment: Calculate expectation and variance to benchmark algorithm efficiency and predict software and hardware reliability.