2018 case study: Difference between revisions
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== Introduction == | == Introduction == | ||
This page will help you organize and understand the | This page will help you organize and understand the 2018 case study. | ||
== The case study == | == The case study == | ||
[[Media: | [[Media:2018 case study.pdf| Click here for the full pdf case study]] | ||
== Terms worth deeply understanding == | == Terms worth deeply understanding == | ||
These articles have been written by students. Any article with a green checkmark [[file:approved.png|25px|Approved]] has been approved by a teacher and can be used reliably to study. | |||
* [[Autonomous]] | * [[Template for student-defined terms]] | ||
* [[Backpropagation]] | * [[Autonomous]] [[file:approved.png|20px|Approved]] | ||
* [[BigO notation]] | * [[Backpropagation]] [[file:approved.png|20px|Approved]] | ||
* [[Bounding boxes]] | * [[BigO notation]] [[file:approved.png|20px|Approved]] | ||
* [[Bounding boxes]] [[file:approved.png|20px|Approved]] | |||
* [[Brute-force]] | * [[Brute-force]] | ||
* [[Convolutional neural networks (CNNs) ]] | * [[Convolutional neural networks (CNNs) ]] | ||
| Line 26: | Line 28: | ||
* [[Greedy algorithm]] | * [[Greedy algorithm]] | ||
* [[Machine learning]] | * [[Machine learning]] | ||
* [[Max-pooling / Pooling]] | * [[Max-pooling / Pooling]] [[file:approved.png|20px|Approved]] | ||
* [[Multi-layer perceptron (MLP)]] | * [[Multi-layer perceptron (MLP)]] | ||
* [[Nearest neighbour algorithm ]] | * [[Nearest neighbour algorithm ]] | ||
* [[Neural networks]] | |||
* [[Overfitting]] | * [[Overfitting]] | ||
* [[Point clouds]] | * [[Point clouds]] | ||
| Line 35: | Line 38: | ||
* [[Society of Automotive Engineers]] | * [[Society of Automotive Engineers]] | ||
* [[Shift invariance (Spatial invariance) ]] | * [[Shift invariance (Spatial invariance) ]] | ||
* [[Vehicle-to-vehicle (VTV) protocol]] | * [[Vehicle-to-vehicle (VTV) protocol]] [[file:approved.png|20px|Approved]] | ||
* [[Vehicle-to-infrastructure (VTI) protocol]] | * [[Vehicle-to-infrastructure (VTI) protocol]] [[file:approved.png|20px|Approved]] | ||
== Previous years case study | == Previous years case study == | ||
* [[2017 case study|Click here for 2017 case study wiki-notes]] | |||
* [[Media:2017 case study.pdf|Click here for the 2017 case study]] | * [[Media:2017 case study.pdf|Click here for the 2017 case study]] | ||
* [[Media:2016 case study test.pdf | Click here for the 2016 case study | <!-- | ||
* [[Media:2016 case study test.pdf | Click here for the 2016 case study]] | |||
--> | |||
* [[Media:2016 case study.pdf | Click here for the 2016 case study]] | * [[Media:2016 case study.pdf | Click here for the 2016 case study]] | ||
Latest revision as of 07:00, 14 April 2018
Case study[1]
Introduction[edit]
This page will help you organize and understand the 2018 case study.
The case study[edit]
Click here for the full pdf case study
Terms worth deeply understanding[edit]
These articles have been written by students. Any article with a green checkmark 
has been approved by a teacher and can be used reliably to study.
- Template for student-defined terms
- Autonomous
- Backpropagation
- BigO notation
- Bounding boxes
- Brute-force
- Convolutional neural networks (CNNs)
- Cost function
- Deep learning
- Dijkstra’s algorithm
- End-to-end learning
- Feature maps (Activation maps)
- Filters (Kernels)
- Filter stride
- Greedy algorithm
- Machine learning
- Max-pooling / Pooling
- Multi-layer perceptron (MLP)
- Nearest neighbour algorithm
- Neural networks
- Overfitting
- Point clouds
- Receptive field
- Sensor Fusion
- Society of Automotive Engineers
- Shift invariance (Spatial invariance)
- Vehicle-to-vehicle (VTV) protocol
- Vehicle-to-infrastructure (VTI) protocol
Previous years case study[edit]
- Click here for 2017 case study wiki-notes
- Click here for the 2017 case study
- Click here for the 2016 case study