CSC311S3: Machine Learning Overview

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Questions and Answers

What is the primary focus of data mining?

Data visualization only

Developing new machine learning algorithms

Analyzing existing data to discover patterns (correct)

Creating new data from scratch

Which method is NOT typically associated with reinforcement learning?

Feed Forward Neural Network

Long Short Term Memory Neural Network

Decision Trees

Clustering algorithms (correct)

In machine learning, which technique is primarily used for dimensionality reduction?

Deep Learning

Reinforcement learning

Principal Component Analysis (correct)

Data mining

What role does data mining aim to fulfill in relation to patterns discovered?

They must be economically advantageous Signup and view all the answers

Which of the following is considered a type of supervised learning?

Naïve Bayes classifier Signup and view all the answers

What class value does the rule set specify for missing values in the outlook attribute?

missing Signup and view all the answers

What method does 1R use to convert numeric attributes into nominal attributes?

Discretization Signup and view all the answers

What is the main disadvantage of using highly branching attributes in 1R?

They can lead to overfitting. Signup and view all the answers

Which of the following best describes the impact of discretization on a dataset?

It increases the number of classes per attribute. Signup and view all the answers

What is the result of using an identification code attribute in a dataset within the context of 1R?

Zero error rate on the training set. Signup and view all the answers

What should be done to avoid overfitting with 1R?

Restrict the number of partitions created. Signup and view all the answers

What adjustment can be made to the breakpoints during discretization to better capture class distributions?

Move breakpoints to where the class changes. Signup and view all the answers

How does 1R handle the presence of missing values?

It treats missing values as a distinct class. Signup and view all the answers

What is the recommendation if the tear production rate is reduced?

none Signup and view all the answers

Under what conditions is a soft lens recommended for a young individual?

when tear production rate is normal and astigmatic is no Signup and view all the answers

What recommendation would apply if a presbyopic individual has a spectacle prescription of hypermetrope and astigmatic is yes?

none Signup and view all the answers

Which attribute does the 1R rule set primarily focus on to make decisions?

age Signup and view all the answers

What is the strategy of the 1R decision tree in terms of classification?

to branch based on majority class occurrences Signup and view all the answers

If a young individual is astigmatic with normal tear production, what is the recommended lens type?

hard Signup and view all the answers

What is the consequence of having a pre-presbyopic person with hypermetrope spectacles and astigmatic yes?

none is recommended Signup and view all the answers

What do the rules of the decision tree primarily address?

the interactions among tear production, age, and astigmatism Signup and view all the answers

Study Notes

Course Overview

Course: CSC311S3/306M3 Machine Learning
Instructor: Prof. A. Ramanan
Institution: Department of Computer Science, University of Jaffna, Sri Lanka
Academic Year: 2021/2022

Course Contents

Introduction to machine learning
Supervised learning
Unsupervised learning
Reinforcement learning
Introduction to Deep Learning
Dimensionality reduction
Experimental setup and evaluation

Assessment Strategy

Focus on reinforcement learning applications
Examples include Google's GNMT, automatic friend tagging, and suggestion systems using neural networks.

Data Mining

Process of discovering patterns in existing data.
Aims to provide meaningful insights leading to advantages, typically economic.
Involves automatic or semi-automatic process and data cleaning steps.

Contact Lenses Data Rules

Decision rules for recommending contact lenses based on attributes such as tear production rate, age, and astigmatism.
Example rules:
- If tear production rate is reduced, recommend none.
- For young patients without astigmatism and normal tear production, recommend soft lenses.

Decision Tree and Inferring Rules

1R algorithm generates one-level decision trees using a single attribute to make predictions.
Rules based on majority class in training data, aiming for high accuracy.
Ties between rule sets are arbitrarily broken to finalize classification.

Handling Missing Values and Numeric Attributes

Missing values are treated as a distinct attribute value.
Numeric attributes can be converted to nominal using discretization methods.
Discretization involves sorting data and placing breakpoints to categorize attributes.

Overfitting Issue

1R method can lead to overfitting by favoring attributes that create many categories.
Attributes with unique values for each instance yield zero error on training data but fail on test data.
Overfitting impacts the model's generalization capability to unseen data.

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Description

This quiz covers the essential topics of Machine Learning as taught in the CSC311S3/306M3 course at the University of Jaffna. Key concepts include supervised and unsupervised learning, reinforcement learning, deep learning, and dimensionality reduction. Prepare to explore the fundamentals and evaluation methods in this exciting field.