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Computer Science
Artificial Intelligence
Introduction to Artificial Intelligence
1. Foundations of Artificial Intelligence
2. Problem Solving and Search
3. Knowledge Representation and Reasoning
4. Machine Learning Fundamentals
5. Neural Networks and Deep Learning
6. Natural Language Processing
7. Computer Vision and Perception
8. AI Ethics and Societal Impact
7.
Computer Vision and Perception
7.1.
Image Formation and Representation
7.1.1.
Camera Models
7.1.1.1.
Pinhole Camera
7.1.1.2.
Lens Systems
7.1.1.3.
Camera Calibration
7.1.2.
Digital Images
7.1.2.1.
Pixel Representation
7.1.2.2.
Color Spaces
7.1.2.3.
Image Formats
7.1.3.
Image Quality
7.1.3.1.
Resolution
7.1.3.2.
Noise
7.1.3.3.
Compression
7.2.
Low-Level Image Processing
7.2.1.
Point Operations
7.2.1.1.
Histogram Manipulation
7.2.1.2.
Contrast Enhancement
7.2.1.3.
Gamma Correction
7.2.2.
Spatial Filtering
7.2.2.1.
Linear Filters
7.2.2.2.
Edge Detection
7.2.2.3.
Noise Reduction
7.2.3.
Morphological Operations
7.2.3.1.
Erosion and Dilation
7.2.3.2.
Opening and Closing
7.2.3.3.
Connected Components
7.3.
Feature Detection and Description
7.3.1.
Edge Detection
7.3.1.1.
Gradient-Based Methods
7.3.1.2.
Canny Edge Detector
7.3.1.3.
Edge Linking
7.3.2.
Corner Detection
7.3.2.1.
Harris Corner Detector
7.3.2.2.
SIFT Features
7.3.2.3.
SURF Features
7.3.3.
Texture Analysis
7.3.3.1.
Texture Descriptors
7.3.3.2.
Local Binary Patterns
7.3.3.3.
Gabor Filters
7.4.
Object Recognition
7.4.1.
Template Matching
7.4.1.1.
Correlation Methods
7.4.1.2.
Normalized Cross-Correlation
7.4.2.
Feature-Based Recognition
7.4.2.1.
Bag of Visual Words
7.4.2.2.
Spatial Pyramid Matching
7.4.3.
Deep Learning Approaches
7.4.3.1.
CNN for Classification
7.4.3.2.
Object Detection Networks
7.4.3.3.
Semantic Segmentation
7.5.
Advanced Vision Tasks
7.5.1.
Image Segmentation
7.5.1.1.
Thresholding
7.5.1.2.
Region Growing
7.5.1.3.
Graph-Based Segmentation
7.5.2.
Object Tracking
7.5.2.1.
Kalman Filters
7.5.2.2.
Particle Filters
7.5.2.3.
Deep Learning Tracking
7.5.3.
3D Vision
7.5.3.1.
Stereo Vision
7.5.3.2.
Structure from Motion
7.5.3.3.
Depth Estimation
7.6.
Speech and Audio Processing
7.6.1.
Audio Signal Basics
7.6.1.1.
Sampling and Quantization
7.6.1.2.
Frequency Domain
7.6.1.3.
Spectrograms
7.6.2.
Speech Recognition
7.6.2.1.
Feature Extraction
7.6.2.2.
Acoustic Modeling
7.6.2.3.
Language Modeling
7.6.2.4.
Decoding
7.6.3.
Speech Synthesis
7.6.3.1.
Text-to-Speech Systems
7.6.3.2.
Concatenative Synthesis
7.6.3.3.
Neural Speech Synthesis
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6. Natural Language Processing
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8. AI Ethics and Societal Impact