CS 440/ECE 448
Fall 2025
Margaret Fleck
Quiz 4 skills list
MP practicalities
Questions related to what you built in MP 5 and 6.
Computer vision
Image formation
- Image formation (pinhole camera, real lenses, human eye)
- Digitization (computer, human, including color and aliasing)
Why might an object or type of object look different in two pictures?
- changes/variation in the object itself
- changes in the 3D scene
- changes in the 2D camera view
Classification:
- Identifying/naming objects in a picture
- Localizing/registering objects within a picture
- Visual question answering, captioning, semantic role labelling for a picture
Reconstructing 3D geometry
- why is it useful?
- from multiple 2D views of a scene
- from a single picture
- from changes in picture (e.g. obstacle avoidance)
Other tasks
- Image generation
- Predicting the future, explaining the past
Classifier Overview
Know brief facts about the following people and systems/models:
- William Labov
- The BERT language model
- CFAR-10 dataset
- Amazon's Mechanical Turk
General design
- Uses for classification: labelling objects, making decisions
- Recall basic classification concepts from early in the term, e.g. train/test/development data, overfitting
- Multi-layer systems
- What can we tune?
- parameters (e.g. weights)
- hyper-parameters (e.g. tuning constants)
- design, network topology
- Batch vs. incremental training
What answer should we produce?
- Challenges with determining the correct answer
- how specific/general should the class label be?
- context may affect best label to choose
- deciding what's important in complex scenes, extended sentences
- Ways to name unfamiliar object
- By parts
- As variants of familiar objects or by analogy with familiar objects
- As modifications of familiar objects (e.g. broken)
Training data
- Supervised training and "gold standard" annotations
- Limitations of "correct" answers/annotation
- Errors even with expert annotators
- Use of amateur annotators (e.g. Mechanical Turk)
- Annotations scraped off the web (e.g. captions for pictures)
- Data available only for final output of system
- Workarounds of limited training data
- Re-purposing layers trained for another purpose
- Creating training pairs by removing information (e.g. color) or masking parts of the input
image or text
- Self-supervised, semi-supervised, unsupervised methods
K-nn and Decision Trees
Specific techniques
- k-nearest neighbors (how it works, what happens if you change k)
- L1 vs. L2 norm
- Decision trees, random forests
- Entropy: definition, how it relates to evaluating possible splits in a decision tree
Perceptrons
- "Linearly separable"
- Basics of how perceptrons work
- Replacing bias with an extra weight
- Overall training algorithm (e.g. epochs, random processing order)
- Rule for updating perceptron weights
- Limitations of perceptrons and ways to address them
- Multi-class perceptrons
- Comparison to Naive Bayes