Machine Learning

This course assumes that you know close to nothing about Machine Learning (ML). Its goal is to give you the concept, the intuitions, and the tools you need to actually implement programs capable of learning from data. Throughout the course, we will cover a large number of techniques, from the simple and the most common used (such as linear regression) to some of the Deep Learning techniques. In this course, we will implement our own toy versions of some algorithms and also use actual frameworks in Matlab and Python. In this course, you will grow your own understanding of ML through concrete examples and a little bit of theory. In the classroom you need to pick up your labtop, experiment with code examples provided in the book, and try your own code. Your participation, attendance, and meeting deadlines are crucially important to benifit from the course. The course contents are as follows:
1. Introduction
2. Supervised learning
3. Unsupervised learning and preprocessing
4. Representing data and engineering features
5. Model evaluation and improvement
6. Dimensionality reduction
7. Artificial neural networks
8. Training deep neural nets
9. Convolutional neural networks
10. Recurrent neural networks

1. Upon completion of the course, students will be expected to:
2. Have a good understanding of the fundamental issues and challenges of machine learning: data, model selection, model complexity, etc.
3. Have an understanding of the strengths and weaknesses of many popular machine learning approaches.
4. Have an understanding of the underlying mathematical relationships within and across Machine Learning algorithms and the paradigms of supervised and un-supervised learning.
5. Be able to design and implement various machine learning algorithms in a range of real-world applications.

Lectures and exercises together covering the entire course. Other supporting material (video, text, etc.) may be used.

Mandatory assigments: None