Informatics Course (4 Credits, 32 hours)

Teaching materials

Handouts

• Course introduction (PDF)
  
• Statistical data analysis (PDF)
• Machine learning and Data Mining: "intelligent" data processing
  
1. Introduction to Machine Learning
   
2. Data exploration | Pre-processing
   
3. Supervised learning (basic concepts | insights)
4. Unsupervised classification (Cluster analysis | Markov clustering | Relaxed clustering)
5. Artificial Neural Networks (basic concepts | insights | 3D Kohonen Map)

Software tools

• The statistical software tool Jamovi (User guide | Getting started | Analyses |
  
• The data analysis and bioinformatics sofyware tool Orange (Getting started | Youtube tutorials | Loading your data | Widget catalog)
• General features | Introduction | Loading your data | Interactive data visualization | Visual programming | Widgets
  
• Widget example (polynomial regression) | The File and Data Table widgets | Example of interactive and selective visualization
  
• Hierarchical clustering: example#1 - example#2 | k-Means clustering | Pivot table | PCA | Feature ranking | Cross-validation | Misclassification | Silhouette plot
• Orange example (OWS file format) of classification on "brown-selected" dataset (TAB file format)
• Bio-tutorial (uses "chemogenomics" and "yeast-interactions" datasets in TAB file format)
• Data Mining COVID-19 Epidemics (dataset CSV file format): Part 1 (PDF - OWS) | Part 2 (PDF - OWS) | Part 3 (PDF - OWS)

Texts, reccomended readings and insights

Websites

Calendar of lessons (online on elearning.unifg.it)

N.B. From 02/03/2022 lessons will be held on the Zoom platform - Meeting ID: 874 3060 8574

Date	From    To		Hours (45')	Topic	Video lessons	Additional material
08/02/2022	15:00	18:00	4	Lesson 1: Course presentation | Data collection and presentation | Data summary measures	1.1 39'18" 1.2 55'56" 1.3 39'37"
16/02/2022	15:00	18:00	4	Lesson 2: Normal and skewed distribution | Hypothesis testing | Choice of statistical test | Correlation and regression | Comparison between groups | From univariate to multivariate analysis	2.1 45'39" 2.2 32'50" 2.3 08'19" 2.4 25'04" 2.5 32'07"
21/02/2022	15:00	18:00	4	Lesson 3: Introduction to Machine Learning | Data exploration and pre-processing	3.1 29'04" 3.2 09'19" 3.3 46'29" 3.4 15'54" 3.5 47'23"
02/03/2022	15:00	18:00	4	Lesson 4: Supervised and unsupervised classification	4.1 67'10" 4.2 62'16" 4.3 21'07" 4.4 36'19" 4.5 44'41" 4.6 19'51"
07/03/2022	15:00	18:00	4	Lesson 5: Artificial Neural Networks	5.1 1h31'28"
16/03/2022	15:00	18:00	4	Lesson 6: The Jamovi software tool for statistical analysis	6.1 21'01" 6.2 42'44" 6.3 34'43"	IPF dataset and Analysis with Jamovi
21/03/2022	15:00	18:00	4	Lesson 7: The Orange data mining software tool (General features, Introduction, Loading data, Interactive visualization, Visual programming, Widgets)	7.1 1h25'26" 7.2 1h3'41"	Dataset and Workflow of example "Loan Status" Dataset of example "Loading Your Data"
30/03/2022	15:00	18:00	4	Lesson 8: Orange widgets (File, Data Table, Clustering, Silhouette plot, Pivot, PCA, Feature ranking, Cross-validation, Misclassification) - Machine learning con Orange, Bio-tutorial