30412  MACHINE LEARNING
Department of Computing Sciences
Course taught in English
Go to class group/s: 25
Course Director:
ANDREA CELLI
ANDREA CELLI
Synchronous Blended: Lezioni erogate in modalitĂ sincrona in aula (max 1 ora per credito online sincrona)
Suggested background knowledge
For an effective learning experience, it is recommended a basic knowledge of Python programming, elementary calculus and linear algebra, probability and statistics.
Mission & Content Summary
MISSION
Machine learning is an exciting and fastmoving field of computer science with many applications such as computer vision, speech recognition, biology and medicine (e.g., predicting proteinprotein interactions, predicting proteinsâ€™ 3D structure), finance, and many consumer applications such as recommending music and movies, and drive autonomous cars. This course provides an introduction to the theoretical foundations and practical algorithms of modern machine learning.
CONTENT SUMMARY
 Basics of optimization
 Supervised learning: linear regression, logistic regression, kernel methods, SVM, decision trees, ensemble methods
 Neural Networks and backpropagation
 Biasvariance tradeoff, model selection
 Unsupervised learning: Principal Component Analysis, hierarchical clustering, kmeans
 Basics of Reinforcement learning
 Implementing Machine Learning Pipelines with sklearn
Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
 State and use the basic results of statistical inference for data analysis.
 Distinguish and characterize the following basic machine learning problems: regression, classification, clustering, dimensionality reduction.
 Describe the basic conceptual ideas, strengths, and limitations of the different learning algorithms for the settings above.
 Evaluate the main tradeoffs such as overfitting, and computational cost vs accuracy.
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
 Implement endtoend machine learning pipelines and rigorously evaluate their performance.
 Demonstrate the ability to cope with realworld data analysis and problem solving (managing, preprocessing and analyze real datasets).
Teaching methods
 Facetoface lectures
 Exercises (exercises, database, software etc.)
 Individual assignments
 Group assignments
DETAILS
 Facetoface lectures focus on the theoretical, methodological, and computational aspects of the topics covered by the course.
 In handson exercise sessions students will work on their laptops to implement solutions to relevant case studies. Students will use Python and common libraries such as scikitlearn.
 Individual/group assignments: each student is required to solve a machine learning problem and provide a written report and the related code.
Assessment methods
Continuous assessment  Partial exams  General exam  


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ATTENDING AND NOT ATTENDING STUDENTS
The exam consists of a theory part and a handson project.
There are two options to pass the exam:
 Option 1: final exam for the whole 31 points. It will consist of questions (closed & open answer) and exercises to be answered on paper. The final exam is closedbook.
 Option 2: final exam (same rules as above) + ML project
 The theory part consists in exercises and questions to be answered on paper. This contributes to 50% of the final grade.
 The project consists in programming code to solve a real ML problem, to be developed individually or in small groups and described through a written report, which is evaluated by the instructors. This contributes to 50% of the final grade.
 In order to pass the exam with Option 2, students must achieve a passing grade in both the theory and the project part (that is, at least 18 points out of 31 in each part). If the student is sufficient in both parts, then the grade is computed as 0.5 X + 0.5 Y, where X and Y are the score obtained in the final written exam and in the project, respectively
 The written exam is the same regardless of whether or not you participate in the challenge
 There is going to be one ML project for each edition of the course. If you participate in the project and obtain a passing grade, you can keep such grade and cumulate it with a final exam done in later sessions (it can be used up to the September session of the same year in which you did the project)
The written exam is used to asses the "knowledge and understanding" learning objectives. The project is used to asses the "applying knowledge and understanding" learning objectives.
Teaching materials
ATTENDING AND NOT ATTENDING STUDENTS
The course mostly relies on the following two books (both available online for free):
 Lindholm, A., Wahlström, N., Lindsten, F., & Schön, T. B. (2022). Machine learning: a first course for engineers and scientists.
 James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An introduction to statistical learning: With applications in Python.
Other useful references:
 C.M. BISHOP (2006). Pattern Recognition and Machine Learning.
 T. HASTIE, R. TIBSHIRANI, J. FRIEDMAN (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction.
Slides and/or notes summarizing the contents presented in class are provided.
Last change 10/11/2023 09:39