Week | Topics | Study Materials | Materials |
1 |
Introduction to Artificial Intelligence and Computational Applied Psychology
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NA
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2 |
Foundations of Artificial Intelligence
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NA
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3 |
R basics (Installations, basic operations, data structure and visualisation)
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To get started with R, you need to install it on your computer. You can download R from the official website (https://www.r-project.org/). Additionally, you may want to use an integrated development environment (IDE) for R, such as RStudio (https://www.rstudio.com/), which provides a more user-friendly interface.
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4 |
Basic R Functions with Explanation and Examples
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Practicing and familiarity with R enviroment
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5 |
Data Manipulation, Statistics and Distributions. Explanation: R provides functions for various probability distributions, such as the normal distribution (dnorm()), Poisson distribution (dpois()), and others. These functions allow you to calculate probabilities and densities for specific distributions.
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Re-generate lab practicing and coding exercise
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6 |
Data Manipulation Functions and Practices
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NA
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7 |
Getting to know secondary data
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Tasks: Load and Inspect Data: Load the “iris” dataset and inspect the first few rows to understand its structure.
Data Summary: Calculate summary statistics for the numeric variables in the dataset, including mean, median, minimum, maximum, and quartiles.
Species Distribution: Determine the distribution of iris species in the dataset. How many observations are there for each species?
Data Visualization: Create a scatter plot to visualize the relationship between sepal length and sepal width. Use different colors or shapes to represent each iris species.
Subset and Compare: Create two subsets of the data: one for versicolor species and one for virginica species. Compare the mean petal length between these two subsets.
Conclusion: Based on the analysis, draw conclusions about the characteristics of the iris dataset and the relationships between different variables.
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8 |
Practice Data Manipulation and Reporting Data
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NA
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9 |
Fall Break
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NA
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10 |
Between Subject ANOVA + Practice exam
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Code practicing
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11 |
More about variance analyses
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NA
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12 |
Natural Language Processing (NLP)
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Lab code practicing and reporting
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13 |
Practical Exam Test
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Exam preparation
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14 |
Recap AI & Psychology
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NA
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15 |
Recap for R coding and Analysis Report
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NA
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