AI has seen a huge boom in the last couple of years. In this talk, we will see an overview of the history and latest trends of the field.

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4. “The question of whether a computer can think is no more
interesting than the question of whether a submarine can swim.”
- Edsger W. Dijkstra
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12. 1. Define problem
2. Prepare data
a. Collection
b. Cleaning
c. Transformation
d. Feature Engineering
3. Choose learning algorithm
4. Train candidate model
5. Evaluate performance
6. Improve results
7. Deploy chosen model
8. Feedback loop
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13. 1. Define problem (object classification: pixels -> object class)
2. Prepare data
a. Collection (web scraping)
b. Cleaning (remove images missing labels)
c. Transformation (reduce resolution)
d. Feature Engineering (normalize pixel values)
3. Choose learning algorithm (convolutional neural network)
4. Train candidate model (gradient descent)
5. Evaluate performance (loss/accuracy)
6. Improve results (hyperparameter optimization)
7. Deploy chosen model (Google Cloud Platform)
8. Feedback loop (keep prototyping with alternative variations of the model)
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