Podcast
Questions and Answers
What is the primary focus of data mining?
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?
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?
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?
What role does data mining aim to fulfill in relation to patterns discovered?
Which of the following is considered a type of supervised learning?
Which of the following is considered a type of supervised learning?
What class value does the rule set specify for missing values in the outlook attribute?
What class value does the rule set specify for missing values in the outlook attribute?
What method does 1R use to convert numeric attributes into nominal attributes?
What method does 1R use to convert numeric attributes into nominal attributes?
What is the main disadvantage of using highly branching attributes in 1R?
What is the main disadvantage of using highly branching attributes in 1R?
Which of the following best describes the impact of discretization on a dataset?
Which of the following best describes the impact of discretization on a dataset?
What is the result of using an identification code attribute in a dataset within the context of 1R?
What is the result of using an identification code attribute in a dataset within the context of 1R?
What should be done to avoid overfitting with 1R?
What should be done to avoid overfitting with 1R?
What adjustment can be made to the breakpoints during discretization to better capture class distributions?
What adjustment can be made to the breakpoints during discretization to better capture class distributions?
How does 1R handle the presence of missing values?
How does 1R handle the presence of missing values?
What is the recommendation if the tear production rate is reduced?
What is the recommendation if the tear production rate is reduced?
Under what conditions is a soft lens recommended for a young individual?
Under what conditions is a soft lens recommended for a young individual?
What recommendation would apply if a presbyopic individual has a spectacle prescription of hypermetrope and astigmatic is yes?
What recommendation would apply if a presbyopic individual has a spectacle prescription of hypermetrope and astigmatic is yes?
Which attribute does the 1R rule set primarily focus on to make decisions?
Which attribute does the 1R rule set primarily focus on to make decisions?
What is the strategy of the 1R decision tree in terms of classification?
What is the strategy of the 1R decision tree in terms of classification?
If a young individual is astigmatic with normal tear production, what is the recommended lens type?
If a young individual is astigmatic with normal tear production, what is the recommended lens type?
What is the consequence of having a pre-presbyopic person with hypermetrope spectacles and astigmatic yes?
What is the consequence of having a pre-presbyopic person with hypermetrope spectacles and astigmatic yes?
What do the rules of the decision tree primarily address?
What do the rules of the decision tree primarily address?
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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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