Grading
There are 4 assignments each worth 15 points and the Final will be worth 40 points.
Syllabus
Class 1
- Check-in and Breakfast
- Overview of Machine Learning concepts and tasks
- Introduction to Supervised and Unsupervised Learning
- Hands-on
- Decision Trees
- Hands-on
- Lunch
- Nearest Neighbor Classification
- Hands-on
Class 2
- Check-in and Breakfast
- Generative Models
- Hands-on
- Guest Lecture
- Lunch
- Regression
- Evaluating Classifiers
- Hands-on
Class 3
- Check-in and Breakfast
- Discriminative Models
- Logistic Regression
- Hands-on
- Lunch
- Perceptron
- Support Vector Machines
- Kernels
- Hands-on
- Richer output spaces
Class 4
- Check-in and Breakfast
- Clustering
- Informative Projections
- Hands-on
- Lunch
- Embeddings, manifold learning and dictionary learning
- Use Cases by Prof. Volkan
- Introduction to Ensemble learning
- Random forests and decision trees
- Bagging and Boosting
- Hands-on
Class 5
- Deep Learning Introduction
- Multilayer Perceptron
- Lunch
- Convolutional Neural Network
- Recurrent Neural Network
- Reinforcement Learning
- Final Review
Final - Date : TBA
- Final Exam