OE-11.1 Introduction to Artificial Neural Networks
You can relate the biological neuron to the artificial one, describe the basic models of ANN connections and the McCulloch-Pitts model, and state the characteristics and applications of ANNs.
The McCulloch-Pitts neuron dates from 1943 and is deliberately crude - fixed weights, a threshold, no learning - which is exactly why it is the right starting point. The biological analogy is a historical motivation, not a claim of equivalence; modern networks resemble brains about as much as aeroplanes resemble birds. If you already know deep learning, treat this unit as the field's origin story rather than as competing material.
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Introduction to artificial neural networks
What these networks are and what they are for, before any mathematics. The honest framing is that they are function approximators, not brains.
The biological neuron and the artificial neuron
The biological cell and the model taken from it, side by side. The model discards almost everything, which is worth noticing rather than glossing over.
Basic concepts of neural networks
The vocabulary the rest of the course uses: weights, layers, activation. Settle these here and the later topics move quickly.
Basic models of ANN connections
The ways units can be wired together, which is the structural half of the subject. The taxonomy in the next topic refines this.
The McCulloch-Pitts model
The first formal model, from long before any of this was practical. It is worth working through because its limitations set the agenda for everything after.
Characteristics of ANNs
What these systems are good at, stated as properties rather than applications. Tolerance to noise and to partial damage are the two that distinguish them.
Applications of ANNs
Where they are actually used. Read for the range, and note how many of the examples are pattern recognition in some form.
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