Course 2024-2025 a.y.

Classes: 45 (II sem.)

Instructors:
Class 45: TO BE DEFINED

Preliminaries to the course are basic probability (at the level of Chapter 6 of the first book mentioned above), very basic calculus and linear algebra, and computing with Python at the level obtained at the course in the first semester. Additionally, this course is in conversation with the concurrent course in Data Analytics and each course will benefit from concepts developed earlier in the other.

The course provides a hands-on introduction to Statistical Machine Learning, the priority is on the implementation of algorithms and the illustration of the ideas using practical examples. All coding is done using Python, in particular numpy and sklearn modules, within jupyter-notebooks. Note that most of the fundamental code used in the course will be provided to the students. The mathematical aspects of statistical machine learning are kept to the minimum.

The course is organized along the following themes:

1. Introduction
   - presentation of the goals of the course; statistics vs machine learning, data science vs artificial intelligence; supervised vs unsupervised machine learning - some case studies and some toy data sets
   - overview of supervised learning by showcasing prediction results and challenges on the case studies and toy examples 
   - models for machine learning; loss functions; learning as an optimization problem

2. Predictive modelling pt 1
   - a basic linear model; learning as a least squares problem; illustrations on case studies and toy examples
   - feature engineering pt 1; models of increasing complexity; evaluating predictive performance pt 1

3. Preprocessing
   - categorical (input/output) variables, transformations, basis functions, data splits
   - case study: predicting with text and images
   - dealing with missing data

4. Predictive modelling pt 2
   - bias-variance tradeoff; best subset selection and  the lasso 
   - optimizing hyperparameters: cross-validation 
   - classification pt 1: main concepts and algorithms
   - classification pt 2: measuring performance and multiclass
   - case studies

5. Smooth lines and curves   
   - regression splines
   
6. Predictive modelling pt 3
   - regression and classification trees: concepts, interpretations and training algorithms
   - bagging, random forests, and ensemble methods

7. Network data and algorithms
   - introduction to networks    
   - network statistics and connectivity properties
   - visualization and community detection
   - basic models for networks
 

At the end of the course student will be able to...

+ understand basic predictive algorithms

+ disinguish between prediction and causal inference

+ appreciate what are missing data and how to deal with them

+ identify network structures

+ analyze network data

At the end of the course student will be able to...

+ build predictive algorithms

+ evaluate predictive performance

+ smooth one and two dimensional data

+ carry out network analytics

• Lectures
• Practical Exercises
• Collaborative Works / Assignments

+ practical exercises in terms of applying algorithms on real and synthetic datasets 

+ collaborative work in terms of a group project on either predictive analytics or network analytics

	Continuous assessment	Partial exams	General exam
Written individual exam (traditional/online)		x	x
Collaborative Works / Assignment (report, exercise, presentation, project work etc.)	x

Teaching materials

ATTENDING AND NOT ATTENDING STUDENTS

Last change 28/05/2024 18:21