Introduction to Embedded Machine Learning complete course is currently being offered by Edge Impulse through Coursera platform and is being taught by Shawn Hymel and Alexander Fred-Ojala.

SKILLS YOU WILL GAIN

- Arduino

- Machine Learning

- Embedded System Design

- Microcontroller

- Computer Programming

Also Check: How to Apply for Coursera Financial Aid


Introduction to Data Analytics for Business (Week 1 - 2) Quiz Answers - Coursera!

Machine Learning and Limitations Quiz Answers

Question 1) Artificial Intelligence (AI) is only concerned with the designing and building of intelligent agents that receive precepts from the environment and discover patterns in that environment.
  • True
  • False

Question 2) Classification is what kind of machine learning?
  • Regression
  • Reinforcement learning
  • Supervised learning
  • Unsupervised learning

Question 3) During the inference phase, the parameters of the rules (or "model") are updated automatically using one or more mathematical algorithms.
  • True
  • False

Question 4) Machine learning models are prone to biases in the data.
  • True
  • False

Question 5) Which of the following are good ways to build societal trust into an artificial intelligence (AI) system? Select all that apply.
  • Robust
  • Efficiency
  • Ethical
  • Lawful

Embedded Machine Learning Quiz Answers

Question 1) A microcontroller is a powerful processing unit that relies on off-chip flash and RAM for computations and is often capable of running general-purpose operating systems, like Windows, macOS, and Linux.
  • True
  • False

Question 2) If a system is 99% accurate at detecting a spoken word, it is deterministic.
  • True
  • False

Question 3) Machine learning can be used to detect anomalies.
  • True
  • False

Question 4) All microcontrollers are capable of running machine learning.
  • True
  • False

Question 5) TinyML software frameworks optimize machine learning operations to allow embedded devices to run machine learning algorithms more quickly and efficiently.
  • True
  • False

Data Collection Quiz Answers

Question 1) Which portion of the dataset is used to evaluate the model in order to fine-tune the model's hyperparameters?
  • Validation set
  • Test set
  • Common set
  • Training set

Question 2) Which portion of the dataset is put aside until the very end to evaluate the model after training and hyperparameter tuning?
  • Test set
  • Training set
  • Validation set
  • Common set

Question 3) Which portion of the dataset is used to automatically update the model's parameters?
  • Common set
  • Training set
  • Test set
  • Validation set

Question 4) A "naïve classifier" always predicts the same class every time it is run.
  • True
  • False

Question 5) What is a "balanced dataset?"
  • A dataset where the input dimensions of the model are equal to the output dimensions.
  • A dataset that contains approximately equal number of samples in the training, validation, and test sets.
  • A dataset that contains samples with the same number of dimensions.
  • A dataset that contains approximately equal number of samples in each class.

Feature Extraction Quiz Answers

Question 1) What is a "feature" with regards to machine learning?
  • A measurable property of something that's observed
  • A plot of the data over time
  • A spectral analysis used to break apart the data into its various frequency components
  • A description of the overall shape and trends in the data

Question 2) Feature extraction can be used to reduce the computational complexity of a machine learning model.
  • True
  • False

Question 3) What is "inference" with regards to machine learning?
  • Automatically updating a model's parameters with data
  • Analyzing the data to determine which features to use
  • Reducing the number of dimensions in features to reduce computational complexity.
  • Using a trained model to make predictions with unseen data

Question 4) Hyperparameters are automatically updated during model training.
  • True
  • False

Question 5) You generally want to use different feature extraction methods between training and inference.
  • True
  • False

Machine Learning Overview Quiz Answers

Question 1) All artificial intelligence (AI) is considered machine learning (ML).
  • True
  • False

Question 2) During the training phase, the parameters of the rules ("model") are updated automatically using one or more mathematical algorithms.
  • True
  • False

Question 3) Machine learning can be used to identify objects in images.
  • True
  • False

Question 4) Ensuring data integrity and privacy is a key factor in building societal trust of AI systems.
  • True
  • False

Question 5) Which of the following would be appropriate uses of embedded machine learning? Select all that apply.
  • Detecting anomalies in an online banking application
  • Detecting anomalies in the servomotor of a satellite
  • Performing an action based on a spoken keyword
  • Identifying objects in an image

Question 6) The test set is used to evaluate the model in order to fine-tune the model's hyperparameters.
  • True
  • False

Question 7) Using a balanced dataset can help you avoid training a naïve classifier.
  • True
  • False

Question 8) Which of the following would be considered a "feature" with regards to machine learning? Select all that apply.
  • How long to train the model
  • Raw data value
  • The root mean square (RMS) value of all the data
  • Prominent frequency values found in the data

Question 9) The process of extracting features from unseen data is known as "inference."
  • True
  • False

Question 10) Machine learning models can learn biases found in data.
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