MLAI01 Taxonomy of ML/AI: What are the different kinds of ML and AI?
MLAI02 Decisions: decision trees; discount rates; expected value; distributions
MLAI03 Classification: The classification problem; standard protocol of training and test; measures of success
MLAI04 Optimization and Goal Attainment: You can distinguish between the use of ML/AI for static optimization/classification and dynamic goal seeking behavior
MLAI05 Q Learning Basics: You can explain the general idea of Q learning and its connection with sequences of decisions, expected value of long term rewards, and discounting
MLAI06 Reinforcement Learning: You can give an account of reinforcement learning, describe applications, etc.

NOTE: Material below this point on this page is work in progress.

Artificial Intelligence (AI): agent that perceives its environment and makes decisions to maximize chances of achieving its goal [1].
- Subfields: vision, robotics, planning, machine learning, etc.
Machine learning: gives “computer the ability to learn without being explicitly programmed” (Arthur Samuel, 1959)

Artificial Neuron
- If the network makes a mistake, adjust the weights and thresholds.
Learning at neuron n:
- If output was 0 but correct answer was 1  for any input i, that was 1, increase wi,n and decrease t.
- If output was 1 but correct answer was 0  for any input that was 1, decrease wi,n and increase t.

From neurons to networks

First artificial neural network, called Perceptron, was invented in 1958 by psychologist Frank Rosenblatt.

It was intended to model how the human brain processed visual data and learned to recognize objects

Machine learning applications are being developed by millions of researchers around the world.
AI systems are becoming more advanced, and a lot of non-possible tasks in the past are now being executed by AI with almost success rate
Machine learning is entering every field: Machine Learning for everything (ML4X).

[1] Maini V, Sabri S. Machine learning for humans. https://medium.com/machine-learning-for-humans/why-machine-learningmatters-6164faf1df12. 2017.