Machine learning
The course addresses components of machine learning such as data, hypothesis space and loss functions and it also introduces algorithms for machine learning: gradient descent, greedy search and linear solvers.
Schedule:
–
Teaching time:
Daytime
Topic:
Information and communications technology
Form of learning:
Online
On-campus
Provider:
Aalto University, FITech
Level:
Intermediate
Credits:
5 By Aalto University (ECTS)
Fee:
€ 0.00
Application period:
1.6.2023
– 29.8.2023
Target group and prerequisites
Matrix algebra, probability theory, basic programming skills.Course description
Course contents
- Exploratory data analysis
- Dimensionality reduction
- PCA
- Regression and classification
- Clustering
- Deep learning
- Reinforcement learning
Learning outcomes
After completing the course, the students
- can formalise applications as ML problems and solve them using basic ML methods
- can perform basic exploratory data analysis
- understand the meaning of the train-validate-test approach in machine learning
- can apply standard regression and classification models on a given data set
- can apply simple clustering and dimensionality reduction techniques on a given data set
- are familiar with and can explain the basic concepts of reinforcement learning.
Teaching methods
The course follows a schedule and includes lectures, self study, assignments, and a project work. The lectures are available online.
Workload
5 credits approx. 134 hours of work divided into:
- Lectures + self-study: 10*(2+3) = 50 hours
- Assignments: 6 * 9 = 54 hours
- Project work: 26 hours
- Peer-grading: 4 hours