Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Introduces applied machine learning concepts such as data collection, feature engineering, model selection, and performance evaluation metrics.
Covers the concept of biometrics, the process of enrolling and verifying biometrics, and the importance of balancing false positives and false negatives.
Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
