Applied Machine Learning

Useful Links

Course Content

Module 1: Learning from Data
Module 2: Representing Data and Features
Module 3: Supervised Machine Learning – Naive Bayes Classifiers, Ensembles of Decision Trees (Random Forests), and Support Vector Machines
Module 4: Neural Networks and Deep Learning
Module 5: Unsupervised Machine Learning – PCA, t-SNE, Agglomerative Clustering, and DBSCAN
Module 6: Model Evaluation, Improvement, and Ethical Aspects

Assignments and Projects

Written Assignment (INL1)
Comparison of the performance of different supervised models
Credits: 1.5
Written Assignment (INL2)
Problem solving with unsupervised learning
Credits: 1
Written Assignment (INL3)
Neural Networks and Deep Learning
Credits: 2
Project (PRO1)
Project Report
Credits: 3