- 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
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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