Lesson 1B Introduction to AI - Pre Quiz
* A famous 19th century proto-computer engineer was
- Charles Barkley
+ Charles Babbage
- Charles Darwin
* Weak AI is a system designed to solve many tasks
- True
+ False
* Chat bots are an example of truly intelligent systems
- false, they are usually designed by a series of rules.
- true, they are usually considered to be 'intelligent
+ false, but they are increasingly able to pass Turing tests as they become more sophisticated.

Lesson 1E Introduction to AI - Post-Quiz
* A top-down approach to AI is a model of reasoning called
- strategic reasoning
+ symbolic reasoning
- synergistic reasoning
* A bottom-up approach to AI is based on neural networks
+ True
- False
* The AI Winter occurred in this era
- 1950s
- 1960s
+ 1970s

Lesson 2B Knowledge Representation and Expert Systems: Pre-Quiz
* The top-down approach to creating intelligent systems was based on:
- knowledge seeking and reading
+ knowledge representation and reasoning
- knowledge reasoning and seeking
* Knowledge is the same as information
- True
+ False
* Knowledge is obtained by an:
+ active learning process
- passive learning process
- both of these

Lesson 2E Knowledge Representation and Expert Systems: Post-Quiz
* The simplest method of knowledge representation is:
+ algorithmic
- symbolic
- synergistic
* Scenarios can represent complex situations that can unfold in time
+ true
- false
* Forward inference starts with initial data and then:
+ executes a reasoning loop
- looks for a goal
- starts over

Lesson 3B Introduction to Neural Networks - Perceptron: Pre-Quiz
* Early neural networks required
+ manual weight adjusting
- terabytes of data
- special reasoning
* A simple neuron is also called a 'threshold logic unit'
+ true
- false
* A perceptron is a ___ type of model
- multi-class classification
- clustering
+ binary classification

Lesson 3E Introduction to Neural Networks - Perceptron: Post-Quiz
* To train a perceptron, find a weights vector that results in the smallest ___.
- size
+ error
- nodes
* To minimize the function of weights, you can use gradient descent
+ true
- false
* During gradient descent, each step updates the ___
- learning rate
+ weights
- gradient

Lesson 4B Neural Networks - Pre Quiz
* The quality of prediction is measured by Loss function 
+ True
- False
* One layer network is capable of classifying ____
- linearly joined classes
+ linearly separable classes
- single layers of classes
* The method of training multi-layered perceptron is called ____
+ back propagation
- multiple propagation
- front propagation

Lesson 4E Neural Networks - Post Quiz
* We use ____ for regression loss functions
- absolute error
- mean squared error
+ all of the above
* All but one is a type of classification loss function
- 0-1 loss
+ binary loss
- logistic loss
* Softmax can be used to convert inputs into probabilities
- True
+ False
* Cross-entropy loss is a function that can calculate similarity between two arbitrary probability distributions
+ True
- False

Lesson 5B Frameworks - Pre Quiz
* Deep Neural Network training requires a lot of computations
+ True
- False
* Overfitting occurs because of ____
- Not enough testing data
+ Too powerful model
- Too much noise in output data
* Bias errors are caused by our ____ not being able to capture the relationship between training data correctly.
- model
+ algorithm
- computer

Lesson 5E Frameworks - Post Quiz
* After compiling our model object, we train by calling ____ function
+ fit
- train
- teach
* Binary cross-entropy is also called log loss
+ True
- False
* Tensorflow is to ____ while PyTorch is to ____
- Facebook, Google
+ Google, Facebook
- Microsoft, Google
* Pred is the values predicted by the network
+ True
- False
