This is my notes for FCAI AI Department Third Year - Second Term Curriculum, hope it benefits you

by: Mario Saber

For each course, you'll find a note corresponding to each lectures, along with expanded explanations for some topics mentions in said lecture, hope this digital garden wouldn't just benefit my colleagues, but anyone who takes these courses

Normal Notes

هي الزتونة, اللي تذاكر منها كملخص في ليلة امتحان او مراجعة سريعة "ساعات بيبقي فيها شرح زيادة لتوضيح سطر او حتة مبهمة متوضحتش في السلايدز"

Expanded Explanations

هنا هتلاقي الأمثلة بتاعت السلايدز أو امثلة من برا, المقارنات, توضيحات زيادة ممكن متكونش متوضحة او مذكورة في السلايدز, دي تقدر تعتمد عليها في مذاكرتك المحاضرة لاول مرة او لو في كونسبت او سواء مفهمتش او نسيت جه منين.

Main sources for these notes: Slides, Gemini Pro, Sheets

Outline

Progress

Progress so far

• Cognitive Science (المنهج خلص)

  • Lecture 1
  • Lecture 2
  • Mobile Robot Localization
  • Bayesian Networks
  • Knowledge Representation
  • Markovian Networks
  • Local Probabilistic
  • Template-Based Representations
  • Kalman Filter
  • Exact Inference: Variable Elimination
  • Hardware (مراجعة أركي)
  • Tour in Robotics Lab

• Supervised Learning

  • Lecture 1 - Intro, KNN, Logistic and Linear Regression
  • Lecture 2 - Naive Bayes, Dimensionality reduction, PCA, and feature selection
  • Lecture 3 - Ensemble Learning
  • Lecture 4 - Optimization Algos
  • Lecture 5 - CNN
  • Lecture 6 - CNN Cont
  • Lecture 7 - Transfer Learning & Auto Encoders
  • Lecture 8 - Learning With Memory

• Reasoning (المنهج خلص)

  • Lecture 1 - Intro
  • Lecture 2 - Predicate and Resolution
  • Lecture 3 - Production Systems
  • Lecture 4 - Examples on Backward and Forward Chaining
  • Lecture 5 - Introduction to Fuzzy Logic
  • Lecture 6 - Neuro - Fuzzy System - Mamdani Approach
  • Lecture 7 - Neuro - Fuzzy System - Takagi and Sugeno’s Approach
  • Lecture 8 - Uncertainty

• Image Processing (المنهج خلص)

  • Lecture 1 - Intro - Ch1
  • Lecture 2 - Digital Images fundamentals - Ch2
  • Lecture 3 - Intensity Transformation and Spatial Filtering - Part1 - Ch3
  • Lecture 4 - Intensity Transformation and Spatial Filtering - Part2 - Ch3
  • Lecture 5 - Intensity Transformation and Spatial Filtering - Part3 - Ch3
  • Lecture 6 - Filtering in the Frequency Domain - Ch4
  • Lecture 7 - Filtering in the Frequency Domain - Part2 - Ch4
  • Lecture 8 - Image Restoration and Reconstruction - Ch5
  • Lecture 9 - Image Segmentation - Part1 - Ch10
  • Lecture 10 - Image Segmentation - Part2 - Ch10
  • Lecture 11 - Morphological Operations - Ch9
  • Lecture 12 - Image Classification - Ch 12

• Multiagent

  • Lecture 1 - Intro
  • Lecture 2 - Agents and Envs - Part1
  • Lecture 3 - Types of Agents
  • Lecture 4 - Solving by Searching
  • Lecture 5 - Example and Midterm
  • Lecture 6 - Uninformed (Blind) Search Strategies
  • Lecture 7 - Informed Search Strategies
  • Lecture 8 - Meta Heuristic Search Strategies
  • Lecture 9 - Searching with nondeterministic actions

• Theory of Mind

  • Lecture 1 - “Mind” vs “Conation” vs “Consciousness”
  • Lecture 2 - Perception
  • Lecture 3 - Epistemology & Human Learning vs AI Types
  • Lecture 4 - Panpsychism, Big Bang, Sub Atomic
  • Lecture 5 - Sub-atomic
  • Lecture 6 -