Class 1:
Introduction to AI and agents, examples: A quick look at Titanic survival prediction
Class 2:
Understanding states and dimensions of models: Pyrobot vacuum cleaner
Class 3:
Generic searching problems: 8-puzzle example
Class 4:
Overview of ML, Supervised learning
Class 5:
In-class presentation by group, selected publications from KDD
Class 6:
Overview of ML, Supervised learning: Decision tree algorithms, entropy
Class 7:
Overview of ML, Unsupervised learning: K-mean clustering
Class 8:
Overview of ML, Feature extraction: Mushroom classification problem, Brightics
Class 9:
Deep learning basics: Introduction to neural networks (NN)
Class 10:
Deep learning basics: Gradient descent and model training/test
Class 11:
Keras/Tensorflow examples: Student admission prediction
Class 12:
Introduction to CNN
Class 13:
AWS Sagemaker exercise (ground truth): Sound classification problem (tentative for now)
Class 14:
Introduction to constrain satisfaction problem (CSP)
Class 15:
Take Home Exam or Group Project Presentation