Introduction to machine learning (BA4)
Summary
Machine learning and data analysis are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and practically implemented.
Content
- Introduction : K nearest neighbors, data representation, basic optimization.
- Linear models : Linear regression, least-square classification, logistic regression, linear SVMs.
- Nonlinear method : Polynomial regression, kernel methods.
- Deep learning : Multi-layer pereceptron, CNNs.
- Unsupervised learning : Dimensionality reduction, clustering.
Keywords
Machine learning, classification, regression, algorithms
Learning Prerequisites
Required courses
Linear algebra
Important concepts to start the course
- Basic linear algebra (matrix/vector multiplications, systems of linear equations, SVD).
- Multivariate calculus (derivative w.r.t. vector and matrix variables).
- Basic programming skills (labs will use Python).
Learning Outcomes
By the end of the course, the student must be able to:
- Define the following basic machine learning problems : regression, classification, clustering, dimensionality reduction
- Explain the main differences between them
- Implement algorithms for these machine learning models
- Optimize the main trade-offs such as overfitting, and computational cost vs accuracy
- Implement machine-learning methods to real-world problems, and rigorously evaluate their performance using cross-validation. Experience common pitfalls and how to overcome them.
Teaching methods
- Lectures
- Lab sessions
Expected student activities
- Attend lectures
- Attend lab sessions and work on the weekly theory and coding exercises
Assessment methods
- Continuous control (graded labs)
- Written final exam
Supervision
| Others | Course website |
In the programs
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Introduction to machine learning (BA4)
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Introduction to machine learning (BA4)
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Introduction to machine learning (BA4)
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Introduction to machine learning (BA4)
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Introduction to machine learning (BA4)
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Introduction to machine learning (BA4)
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks