(We are sorry, registrations are closed. Full capacity has been reached. Those who registered will be contacted by organizers for more details about the mini course.)
The University of Vlora will host the Vlora Mini Course on Data Science and Artificial Intelligence from May 19 to May 23, led by faculty from American University’s Department of Math & Stat. The required background: a one semester introductory course on statistics. Those attending all the sessions of the mini course will have the option of receiving a certificate of participation.
The program consists of ten modules - two modules per day. Each module will last 90 minutes - the first 30 minutes will discuss some theoretical material, the rest is group work and hands on experience.
Participants will be required to bring their own laptop. We will work in the cloud, so downloading software will not be necessary.
Here is a detailed description of the topics we will cover during the mini course:
Module one: Get Data Introduction of the Data Science Life Cycle.
Introduction to RStudio in Posit Connect
Sources of data to include Open data.
Different types of data formats: .csv files, excel files, urls, compressed data, Arrow-Parquet
Rectangular data, vectors and data frames in R
Methods for getting rectangular data from other sources or files into R data frames.
Viewing data and getting summary statistics about data in R data frames
Module two: Clean and reshape data
Cleaning data
Filtering rows using logical comparisons and %in%.
Selecting columns using names and tidyselect.
Reshaping data using pivots.
Module three: Visualize Univariate Data
Variable types: continuous vs categorical
Plots for continuous variables
Plots for categorical variables
Customizing plots
Example of dynamic titles
Module four: Visualizing Multi-Variate data
Create bivariate point plots and box plots.
Use plot aesthetics to code by additional variables.
Add and interpret linear and non-linear smoothers.
Module five: Statistical tests and models
Why statistical tests and understanding a Null hypothesis.
Interpreting a p-value.
Using and interpreting the t.test function, aov/anova, and lm functions in R.
Module six: Classification Models
Binary Classification
Logistic Regression
Confusion Matrices and metrics
Module seven: Overfitting and Bias-Variance Tradeoff
Degrees of Freedom and Restrictive versus flexible models
Over-fitting
Bias-Variance Trade off
Validating and models by splitting data.
Module eight: Evaluating and Tuning Models
Optimizing Loss Functions
Evaluation Metrics
Variable Selection and Partial F-Tests
ROC Curves in Classification
Module nine: Neural networks
Convergence of big data, big processing power (GPUs), and algorithmic advances.
Neural Network architecture
Role of Activation Functions and gradient descent and back-propagation.
Challenges in Over fitting and convergence
Module ten: Generative AI and prompt engineering
Generative AI compared to predictive models.
Tokenization and Embedding
Semantic similarity for prediction.
Dangers of overfitting and the need for regularization.
Elements of prompt engineering.
