Pattern Recognition and Machine Learning notes (1st edition)

Although the course has recommended literature, the questions on the exam will only be about the lectures. Therefore, I made a summary of my lecture notes. It gives an overview of the course. If you study this, together with the homework, you'll be more than ready for the exam.

 Machine Learning: Design and development of algorithms for training computers with example data or past experience based on a performance criteria.  Key concepts: • Example Data: Observed instances possibly with known outcome • Training/Learning: Process of generalization from the example data as probability distributions, rules, discriminative functions...

 Machine Learning: Design and development of algorithms for training computers with example data or past experience based on a performance criteria.  Key concepts: • Example Data: Observed instances possibly with known outcome • Training/Learning: Process of generalization from the example data as probability distributions, rules, discriminative functions...

Although the course has recommended literature, the questions on the exam will only be about the lectures. Therefore, I made a summary of my lecture notes. It gives an overview of the course. If you study this, together with the homework, you'll be more than ready for the exam.

This is a solution manual for pattern recognition and machine learning. It contains detailed solutions for the questions in the relevant book. Helpful for understanding and verifying the approach and correctness of solutions.