diff --git a/1-Intro/README.md b/1-Intro/README.md
index 9aedf7bc..069e27be 100644
--- a/1-Intro/README.md
+++ b/1-Intro/README.md
@@ -4,7 +4,7 @@
> Sketchnote by [Tomomi Imura](https://twitter.com/girlie_mac)
-## [Pre-lecture quiz](link-to-quiz-app) TODO
+## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/0)
**Artificial Intelligence** is an exciting scientific discipline that studies how we can make computers exhibit intelligent behavior, e.g. do those things that human beings are good at doing.
@@ -140,7 +140,7 @@ Over the past few years we have witnessed huge successes with large language mod
Do a tour of the internet to determine where, in your opinion, AI is most effectively used. Is it in a Mapping app, or some speech-to-text service or a video game? Research how the system was built.
-## [Post-lecture quiz](link-to-quiz-app) TODO
+## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/1)
## Review & Self Study
diff --git a/2-Symbolic/README.md b/2-Symbolic/README.md
index 01251f03..6706a50f 100644
--- a/2-Symbolic/README.md
+++ b/2-Symbolic/README.md
@@ -6,7 +6,7 @@
The quest for artificial intelligence is based on a search for knowledge, to make sense of the world similar to how humans do. But how can you go about doing this?
-## [Pre-lecture quiz](link-to-quiz-app) TODO
+## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/2)
In the early days of AI, the top-down approach to creating intelligent systems (discussed in the previous lesson) was popular. The idea was to extract the knowledge from people into some machine-readable form, and then use it to automatically solve problems. This approach was based on two big ideas:
@@ -90,7 +90,7 @@ Simplified structure of a human neural system | Architecture of a knowledge-base
Expert systems are built like the human reasoning system, which contains **short-term memory** and **long-term memory**. Similarly, in knowledge-based systems we distinguish the following components:
* **Problem memory**: contains the knowledge about the problem being currently solved, i.e. the temperature or blood pressure of a patient, whether he has inflammation or not, etc. This knowledge is also called **static knowledge**, because it contains a snapshot of what we currently know about the problem - the so-called *problem state*.
-* **Knowledge base**: represents long-term knowledge about a problem domain. It is extracted manually from human experts, and does not change from consulation to consultation. Because it allows us to navigate from one problem state to another, it is also called **dynamic knowledge**.
+* **Knowledge base**: represents long-term knowledge about a problem domain. It is extracted manually from human experts, and does not change from consultation to consultation. Because it allows us to navigate from one problem state to another, it is also called **dynamic knowledge**.
* **Inference engine**: orchestrates the whole process of searching in the problem state space, asking questions of the user when necessary. It is also responsible for finding the right rules to be applied to each state.
As an example, let's consider the following expert system of determining an animal based on its physical characteristics:
@@ -220,7 +220,7 @@ Nowadays, AI is often considered to be a synonym for *Machine Learning* or *Neur
In the three notebooks associated to this lesson, there are challenges at the end - pick one, and try to solve it!
-## [Post-lecture quiz](link-to-quiz-app) TODO
+## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/3)
## Review & Self Study
diff --git a/3-NeuralNetworks/03-Perceptron/README.md b/3-NeuralNetworks/03-Perceptron/README.md
index 5874bb67..df2d7e01 100644
--- a/3-NeuralNetworks/03-Perceptron/README.md
+++ b/3-NeuralNetworks/03-Perceptron/README.md
@@ -1,6 +1,6 @@
# Introduction to Neural Networks: Perceptron
-## [Pre-lecture quiz](link-to-quiz-app) TODO
+## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/4)
One of the first attempts to implement something similar to a modern neural network was done by Frank Rosenblatt from Cornell Aeronautical Laboratory in 1957. It was a hardware implementation called "Mark-1", designed to recognize primitive geometric figures, such as triangles, squares and circles.
@@ -14,7 +14,7 @@ An input image was represented by 20x20 photocell array, so the neural network h
> ✅ A potentiometer is a device that allows the user to adjust the resistance of a circuit.
-> New York Times wrote about perceptron at that time:
+> The New York Times wrote about perceptron at that time:
> *the embryo of an electronic computer that [the Navy] expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence.*
## Perceptron Model
@@ -30,7 +30,7 @@ where f is a step activation function
## Training the Perceptron
-To train a perceptron we need to find a weights vector w that classifies most of the values correctly, i.e. results in the smallest **error**. This error is defined by **perceptron criterion** on the following manner:
+To train a perceptron we need to find a weights vector w that classifies most of the values correctly, i.e. results in the smallest **error**. This error is defined by **perceptron criterion** in the following manner:
E(w) = -∑wTxiti
@@ -43,7 +43,7 @@ This criteria is considered as a function of weights w, and we need to minimize
w(t+1) = w(t) - η∇E(w)
-Here η is so-called **learning rate**, and ∇E(w) denotes the **gradient** of E. After we calculate the gradient, we end up with
+Here η is the so-called **learning rate**, and ∇E(w) denotes the **gradient** of E. After we calculate the gradient, we end up with
w(t+1) = w(t) + ∑ηxiti
@@ -77,7 +77,7 @@ In this lesson, you learned about a perceptron, which is a binary classification
If you'd like to try to build your own perceptron, try [this lab on Microsoft Learn](https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-averaged-perceptron?WT.mc_id=academic-57639-dmitryso) which uses the [Azure ML designer](https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer?WT.mc_id=academic-57639-dmitryso).
-## [Post-lecture quiz](link-to-quiz-app) TODO
+## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/5)
## Review & Self Study
diff --git a/quiz-app/src/assets/translations/en/group-1.json b/quiz-app/src/assets/translations/en/group-1.json
index 711b1f9d..b9fb9c6c 100644
--- a/quiz-app/src/assets/translations/en/group-1.json
+++ b/quiz-app/src/assets/translations/en/group-1.json
@@ -109,6 +109,218 @@
]
}
]
+ },
+ {
+ "id": 2,
+ "title": "Knowledge Representation and Expert Systems: Pre-Quiz",
+ "quiz": [
+ {
+ "questionText": "The top-down approach to creating intelligent systems was based on:",
+ "answerOptions": [
+ {
+ "answerText": "knowledge seeking and reading",
+ "isCorrect": "false"
+ },
+ {
+ "answerText": "knowledge representation and reasoning",
+ "isCorrect": "true"
+ },
+ {
+ "answerText": "knowledge reasoning and seeking",
+ "isCorrect": "false"
+ }
+ ]
+ },
+ {
+ "questionText": "Knowledge is the same as information",
+ "answerOptions": [
+ {
+ "answerText": "true",
+ "isCorrect": "false"
+ },
+ {
+ "answerText": "false",
+ "isCorrect": "true"
+ }
+ ]
+ },
+ {
+ "questionText": "Knowledge is obtained by an:",
+ "answerOptions": [
+ {
+ "answerText": "active learning process",
+ "isCorrect": "true"
+ },
+ {
+ "answerText": "passive learning process",
+ "isCorrect": "false"
+ },
+ {
+ "answerText": "both of these",
+ "isCorrect": "false"
+ }
+ ]
+ }
+ ]
+ },
+ {
+ "id": 3,
+ "title": "Knowledge Representation and Expert Systems: Post-Quiz",
+ "quiz": [
+ {
+ "questionText": "The simplest method of knowledge representation is:",
+ "answerOptions": [
+ {
+ "answerText": "algorithmic",
+ "isCorrect": "true"
+ },
+ {
+ "answerText": "symbolic",
+ "isCorrect": "false"
+ },
+ {
+ "answerText": "synergistic",
+ "isCorrect": "false"
+ }
+ ]
+ },
+ {
+ "questionText": "Scenarios can represent complex situations that can unfold in time",
+ "answerOptions": [
+ {
+ "answerText": "true",
+ "isCorrect": "true"
+ },
+ {
+ "answerText": "false",
+ "isCorrect": "false"
+ }
+ ]
+ },
+ {
+ "questionText": "Forward inference starts with initial data and then:",
+ "answerOptions": [
+ {
+ "answerText": "executes a reasoning loop",
+ "isCorrect": "true"
+ },
+ {
+ "answerText": "looks for a goal",
+ "isCorrect": "false"
+ },
+ {
+ "answerText": "starts over",
+ "isCorrect": "false"
+ }
+ ]
+ }
+ ]
+ },
+ {
+ "id": 4,
+ "title": "Introduction to Neural Networks - Perceptron: Post-Quiz",
+ "quiz": [
+ {
+ "questionText": "Early neural networks required",
+ "answerOptions": [
+ {
+ "answerText": "manual weight adjusting",
+ "isCorrect": "true"
+ },
+ {
+ "answerText": "terabytes of data",
+ "isCorrect": "false"
+ },
+ {
+ "answerText": "special reasoning",
+ "isCorrect": "false"
+ }
+ ]
+ },
+ {
+ "questionText": "A simple neuron is also called a 'threshold logic unit'",
+ "answerOptions": [
+ {
+ "answerText": "true",
+ "isCorrect": "true"
+ },
+ {
+ "answerText": "false",
+ "isCorrect": "false"
+ }
+ ]
+ },
+ {
+ "questionText": "A perceptron is a ___ type of model",
+ "answerOptions": [
+ {
+ "answerText": "linear regression",
+ "isCorrect": "false"
+ },
+ {
+ "answerText": "clustering",
+ "isCorrect": "false"
+ },
+ {
+ "answerText": "binary classification",
+ "isCorrect": "true"
+ }
+ ]
+ }
+ ]
+ },
+ {
+ "id": 5,
+ "title": "Introduction to Neural Networks - Perceptron: Post-Quiz",
+ "quiz": [
+ {
+ "questionText": "To train a perceptron, find a weights vector that results in the smallest ___.",
+ "answerOptions": [
+ {
+ "answerText": "size",
+ "isCorrect": "false"
+ },
+ {
+ "answerText": "error",
+ "isCorrect": "true"
+ },
+ {
+ "answerText": "nodes",
+ "isCorrect": "false"
+ }
+ ]
+ },
+ {
+ "questionText": "To minimize the function of weights, you can use gradient descent",
+ "answerOptions": [
+ {
+ "answerText": "true",
+ "isCorrect": "true"
+ },
+ {
+ "answerText": "false",
+ "isCorrect": "false"
+ }
+ ]
+ },
+ {
+ "questionText": "During gradient descent, each step updates the ___",
+ "answerOptions": [
+ {
+ "answerText": "learning rate",
+ "isCorrect": "false"
+ },
+ {
+ "answerText": "weights",
+ "isCorrect": "true"
+ },
+ {
+ "answerText": "gradient",
+ "isCorrect": "false"
+ }
+ ]
+ }
+ ]
}
]
}