This course introduces data science and machine learning using R. It combines theoretical lectures with hands-on labs and case studies to provide a comprehensive learning experience.
There are two .zip files with slides and labs.
| Lecture | Topics | Lab / Practical | Assessment / Notes |
|---|---|---|---|
| 1 | Introduction to AI, ML, and Big Data; Data types and sources | Lab 1: R commands; Create PDF, HTML, PPT | |
| 2 | Data handling: missing values, statistical description, visualization | Lab 2: RMarkdown | |
| 3 | Linear regression, Decision Trees, Random Forest | Lab 3: Random Forest; Bike Sharing Demand | Midterm Exam 1 (Tuesday during class) |
| 4 | Model training and evaluation with caret; Data preprocessing; Cross-validation | ||
| 5 | Artificial Neural Networks (ANN); Multilayer Perceptron (MLP) | Lab 4: Training and tuning with caret; MLP for regression | |
| 6 | Classification problems: MNIST image recognition | ||
| 7 | Convolutional Neural Networks (CNNs); Deep learning with Fashion dataset | Lab 5: MLP for classification; CNNs | Midterm Exam 2 (Tuesday in Aula 30, Bunker) |
| 8 | Web Scraping | Lab 6: Web scraping | Assignment Release (April 15): 5 tasks + peer assessment, 40 points total |
| 9 | Sentiment Analysis | Lab 7: Sentiment analysis | Task 1 discussion and example presentation |
| 10 | Accessing APIs in R | Lab 8: APIs in R | Tasks 2–4: Pitching video (4 pts), Oral presentation (8 pts), Questions (4 pts); Oral presentations Tuesday & Wednesday |