Get introduced to the world of artificial intelligence and how machines learn based on images and big data.

Course gives basic introduction to artificial intelligence and deep learning methods, and their usage in vision-based applications. Common workflows are covered, including image classification and object detection. Participants would be able to select neural network models, and understand their configuration via training parameters, structure and other strategies.

Course topics:

• Artificial intelligence basics

• Machine learning vs deep learning

• Neural network training

• Parameters and hyperparameters

• Common neural network models and training strategies

• Object detection

• Convolutional neural networks

• Image segmentation

• Neural network deployment and performance optimization

• Application of neural network in programming workflows

• Practical models and environments: UNET, Caffe, DIGITS, TensorFlow, Jetson AGX

Hardware (required): Computer with Internet connection, working speakers and microphone.

Software: Chrome browser.

Course Typically Offered: Live Online in Fall quarter (mid June - August), Winter quarter (mid January - March), Spring quarter (April - mid June) or Summer quarter (mid June - August).

Prerequisites: Students should have basic engineering knowledge gained from either industry experience or appropriate level of undergraduate studies.

Next Step: To gain additional knowledge in how the AI can be applied in specific domains, such as e.g. in autonomous driving, consider taking courses from the Automotive Engineering catalog (NIT-AU-XX).

Course Number: NIT-AI-01

Duration: 2.00 units (~20 live teaching hours, ~40 hours of individual practice and preparation work)