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UTK Notes


Quiz #1

Question 1

Which topics are covered in the COSC 325: Introduction to Machine Learning course?

A. Operating systems, database management, and software engineering.
B. Quantum computing, blockchain technology, and artificial intelligence ethics.
C. Cybersecurity, network protocols, and cloud computing.
D. Clustering, decision trees, neural network learning, statistical learning methods, Bayesian learning methods, dimension reduction, kernel methods, and reinforcement learning.

Answer D. Clustering, decision trees, neural network learning, statistical learning methods, Bayesian learning methods, dimension reduction, kernel methods, and reinforcement learning.

Question 2

What is the weight of the course project in the overall grade?

A. 15%
B. 10%
C. 20%
D. 25%

Answer C. 20%

Question 3

According to Tom Mitchell’s definition, what three components are necessary for a computer program to be considered learning?

A. Input I, processing P, and output O
B. Model M, parameters P, and training T
C. Task T, performance measure P, and experience E
D. Data D, algorithm A, and output O

Answer C. Task T, performance measure P, and experience E
Lecture Slide 14: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E." — Tom Mitchell, Professor at Carnegie Mellon University

Question 4

According to the syllabus, what is the recommended background for this course?

A. Advanced calculus and linear algebra
B. Java programming and data structures
C. None, this is an introductory course
D. Basic statistics, calculus, linear algebra, and Python programming

Answer D. Basic statistics, calculus, linear algebra, and Python programming
Course Information section:
  • Recommended Background
    • Python programming, including Numpy, and Pandas.
    • Basic calculus, including differentiation and the chain rule.

Question 5

What resources are recommended for this course?

A. The Hundred-Page Machine Learning Book, Andriy Burkov, 2024
B. Machine Learning Yearning by Andrew Ng
C. Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig
D. Deep Learning by Ian Goodfellow

Answer A. The Hundred-Page Machine Learning Book, Andriy Burkov, 2024
Recommended Resources section:
  • The Hundred-Page Machine Learning Book, Andriy Burkov, 2024
  • A Course in Machine Learning, 2017, H. Daume III
  • Introduction to Machine Learning, 2021, Sebastian Rashka
  • Probabilistic Machine Learning: An Introduction, 2022, Kevin Murphy
  • An Introduction to Statistical Learning, 2023, James et. al.
  • Dive Deep Into Deep Learning, 2023, Aston Zhang
  • Understanding Machine Learning: From Theory to Algorithms, 2014, Shai Shalev-Shwartz and Shai Ben-David

Question 6

What are the office hours for Dr. Hector J. Santos-Villalobos?

A. Mondays 9:00 am - 10:00 am and Wednesdays 1:00 pm - 2:00 pm
B. Tuesdays 10:00 am - 11:00 am and Fridays 2:00 pm - 3:00 pm
C. Mondays 2:00 pm - 3:00 pm and Wednesdays 10:00 am - 11:00 am
D. Tuesdays 12:50 - 1:30 pm and Thursdays 10:10 am-11:10 am

Answer D. Tuesdays 12:50 - 1:30 pm and Thursdays 10:10 am-11:10 am Instructor Information section:
  • Office Hours: Tuesdays 12:50 - 1:30 pm and Thursdays 10:10 am-11:10 am
    • I will be available in person, Zoom, or MS Teams.
    • You can also contact me through Discord. More details below.
    • Please email me if you would like to meet at any other time.
    • Office hours are not meant solely to answer class questions. Feel free to stop by for general academic and professional guidance.

Question 7

What is a key characteristic of supervised learning?

A. It is primarily used for reinforcement tasks.
B. It involves learning from unlabeled data.
C. It requires labeled data to learn the mapping from input to output.
D. It includes techniques like clustering and dimensionality reduction.

Answer C. It requires labeled data to learn the mapping from input to output.

Question 8

In the context of machine learning, what is generalization?

A. The process of increasing the complexity of the model.
B. The technique of reducing the number of features in the dataset.
C. The ability of a model to apply learned knowledge to new, unseen data.
D. The ability of a model to memorize training data perfectly.

Answer C. The ability of a model to apply learned knowledge to new, unseen data.
Lecture Slide 18

Question 9

Which type of machine learning does clustering belong to?

A. Semi-supervised learning
B. Supervised learning
C. Unsupervised learning
D. Reinforcement learning

Answer C. Unsupervised learning
Lecture Slide 27

Question 10

What is a key benefit of using Python for machine learning as discussed in the lecture?

A. It is the fastest programming language.
B. It requires no prior programming knowledge.
C. It has extensive libraries and frameworks for machine learning.
D. It is the only language supported by machine learning algorithms.

Answer C. It has extensive libraries and frameworks for machine learning.
Lecture Slide 49