# PSY 891: Introduction To Machine learning

The goal of this course is to understand artificial neural networks and how they learn. These concepts are extremely relevant to machine learning and artificial intelligence, but from my perspective, they are also at-least as crucially relevant to psychology. Our course, while very relevant to computer science, data science, mathematics, and philosophy, is above all else, a course in (computational) psychology. Selected lectures available below.

Lecture 1 Covers John Conway's Game of Life (1:10 mark) and an Overview of Programming in R

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Lecture 2 Covers the 'line of best fit' and the optim ( ) function in R. We also provide a high level intro to partial differentiation useful later in the course

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Lecture 3 Covers the pdfs, cdfs, The Central Limit Theorem, and The Law of Large Numbers. We describe how bootstrapping and permutation methods may decrease our reliance on traditional regression that requires assumptions that may not always hold true.

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