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About This Course
This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These concepts are exercised in supervised learning and reinforcement learning, with applications to images and to temporal sequences.
Learning Objectives
This course includes lectures, lecture notes, exercises, labs, and homework problems.
Recommended Prerequisites
Computer programming (python); Calculus; Linear Algebra
This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These concepts are exercised in supervised learning and reinforcement learning, with applications to images and to temporal sequences.
Learning Objectives
- Understand the formulation of well-specified machine learning problems
- Learn how to perform supervised and reinforcement learning, with images and temporal sequences.
This course includes lectures, lecture notes, exercises, labs, and homework problems.
Recommended Prerequisites
Computer programming (python); Calculus; Linear Algebra
