Module Indicative Content |
Computer Vision
We will study how to use open source computer vision to detect objects in images, edges, regions of interest and find lanes on video.
|
Perceptrons
Use perceptrons to classify a linearly separable set of inputs.
|
Deep Neural Networks
Will will study deep neural networks to help us extract features from images.
|
Multiclass Classification
We will study how to train a model to fit more than just two classes. We will use this later to classify images.
|
Image recognition
We will use openly available image datasets to see how to train, validate and test image recognition.
|
Convolutional Nueral Networks
Convolutional neural networks are the go-to model for image classification. We will study these efficient way to extract features
from images.
|
Classifying Images
We will study how to process data to train a neural network to classify road signs.
|
Polynomial regression
We will learn how to build a model to fit a continuous dataset.
|
Behavioural Cloning
We will use the convolutional neural network to learn from training data.
|
Building Robots
We will cover how to build a robot with simple sensors, cheap computers and open source tools.
|
DKIT reserves the right to alter the nature and timings of assessment
Module Resources
|
Recommended Book Resources |
---|
-
Lance Eliot. (2018), Lance Eliot 2018, Introduction to Driverless Self-Driving Cars: The Best AI Insider.
-
Packt. (2018), Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras.
-
Lance Eliot. (2017), New Advances in AI Autonomous Driverless Self-Driving Cars: Artificial Intelligence.
-
Packt. (2017), Machine Learning for OpenCV.
|
This module does not have any article/paper resources |
---|
This module does not have any other resources |
---|