Studied by 1574 PeopleTags & Description
Artificial Intelligence (AI)
The capability of a machine to imitate intelligent human behavior.
NOT all artificial intelligence is machine learning.
Found in Lecture ML Part 1 - Machine Learning
Machine Learning
Giving computers the ability to learn without being explicitly programmed.
All machine learning is artificial intelligence.
Found in Lecture ML Part 1 - Machine Learning
Deep Learning
Creating machine learning models using deep neural networks.
Found in Lecture ML Part 1 - Machine Learning
Supervised Learning
Teach the machine by providing examples (with labels).
Provide examples to the machine and then let the machine learn a model that represents the examples. Then use the model to predict values for data that are unknown.
Found in Lecture ML Part 1 - Machine Learning
Unsupervised Learning
Try to recognize patterns in the data without any examples (no labels).
Provide features to the machine and then let the machine determine the patterns that exist in the data.
Found in Lecture ML Part 1 - Machine Learning
Reinforcement Learning
Perform an action then learn based on a reward or punishment.
Reinforcement Learning considers definition of states, a set of possible actions that can be taken, and a reward score.
For a given state AND the action the machine takes, it receives a positive or negative reward.
Found in Lecture ML Part 2 - Machine Learning
Exploration vs. Exploitation in Reinforcement Learning
The core idea in reinforcement learning is trying to balance between trying new things and using what you know.
In training, a model should have a high exploration factor so it can learn.
When deployed, a model should focus on applying the knowledge it learned.
Found in Lecture ML Part 2 - Machine Learning
Exploration factor
Parameter that determines whether you should try something new.
Found in Lecture ML Part 2 - Machine Learning
Exploitation factor
Parameter that determines whether you should use the knowledge you already have.
Found in Lecture ML Part 2 - Machine Learning
Features
The values that describe the data (e.g., x values).
Found in Lecture ML Part 1 - Machine Learning
Label
The value that you are trying to determine (e.g., y-values).
Also called the target class.
Found in Lecture ML Part 1 - Machine Learning
Classification
A type of supervised learning.
Target class has discrete values (e.g. dog, cat, mouse).
Found in Lecture ML Part 1 - Machine Learning
Regression
A type of supervised learning.
Target class has continuous values.
Found in Lecture ML Part 1 - Machine Learning
K-Means Clustering
One example of how to do unsupervised learning.
Cluster the dataset into k groups.
Start with k points (centroids)
Determine which cluster a point belongs (closest centroid)
Move centroid to the average of all points in that cluster
Repeat 2&3 until convergence
Found in Lecture ML Part 1 - Machine Learning
Centroid
Center of mass ("average" of the points).
Found in Lecture ML Part 1 - Machine Learning
Neural Networks
A neural network is a network of neurons (analogous to biological neurons in our brains).
Neural Network forms the core of every Deep Learning algorithm. These networks are represented as systems of interconnected "neurons", which send messages to each other.
Found in Lecture ML Part 3 - Machine Learning
Neurons
Each neuron has the following:
Multiple inputs with a weight for each input
An activation function
An output
Found in Lecture ML Part 3 - Machine Learning
Value Iteration
Finding the expected eventual reward of being in each state.
Q-Learning
Starts with an unknown set of rewards and learns each value by doing.
Neuron Step-Activation "Algorithm"
Multiply each input by its weight
Sum up all the products
If the sum is >= the threshold value, output 1
Else, output 0
Training a Neural Network
Start with random weights (or educated guess) and produce an incorrect result
Determine the error relative to correct results
Update the weights to account for the error
Repeat Steps 2 & 3 until the output converges to the correct result