Computer Science 389 - Intro to Machine Learning

The course provides an introduction to machine learning algorithms and applications. Machine learning algorithms answer the question: 'How can a computer improve its performance based on data and from its own experience?' The course is roughly divided into thirds: supervised learning (learning from labeled data), reinforcement learning (learning via trial and error), and real-world considerations like ethics, safety, and fairness. Specific topics include linear and non-linear regression, (stochastic) gradient descent, neural networks, backpropagation, classification, Markov decision processes, state-value and action-value functions, temporal difference learning, actor-critic algorithms, the reward prediction error hypothesis for dopamine, connectionism for philosophy of mind, and ethics, safety, and fairness considerations when applying machine learning to real-world problems. This course counts as an Elective for the INFORM Major.

Open to juniors and seniors in Computer Science or Informatics. CS 220/230&240/STAT515MATH233 PREVIOUSLY COMPSCI 390A. STUDENTS NEEDING SPECIAL PERMISSION MUST REQUEST OVERRIDES VIA ON-LINE FORM: https://www.cics.umass.edu/overrides.