4C16/5C16 is course on Machine Learning (ML), with a focus on Deep Learning. It is a fourth and fifth year module offered by the Electronic & Electrical Engineering department to the undergraduate students of Trinity College Dublin.
Although Deep Learning has been around for quite a while, it has recently become a disruptive technology that has been unexpectedly taking over operations of technology companies around the world and disrupting all aspects of society. When you read or hear about AI or machine Learning successes in the news, it really means Deep Learning successes.
The course starts with an introduction to some essential aspects of Machine Learning, including Least Squares, Logistic Regression and a quick overview of some popular classification techniques.
Then the course dives into the fundamentals of Neural Nets, including Feed Forward Neural Nets, Convolution Neural Nets and Recurrent Neural Nets.
The material is constructed in collaboration with leading industrial practitioners including Google, YouTube and Movidius, and students will have guest lectures from these companies.
We have designed a unique environment specifically for this course so that students can learn best industry practices.
Our web platform can transparently connect students to a Google Cloud Platform cluster via web based terminal/editor/Jupyter sessions. Labs use the Keras framework and are automatically assessed using Git to give immediate feedback.
Labs include designing and training various DNN for image classification challenges, self driving car (simulator) and text processing.
handouts and videos from last years an be found here (2017/18) and here (2018/19). This year the lectures will not be recorded.
It is recommended to students to refresh their knowledge of Python 3 prior to starting 4C16. Some useful resources are listed in the document below:
Hugh Denman's slides about python 3 and the 4C16 lab system is available here:
George Sterpu is compiling a list of frequently asked questions & answers on his webpage:
- pdf slides
- here is quick note detailing the discusssion on loss and error distribution.
- pdf tutorial on linear algebra
- pdf tutorial on least squares
- solution-tutorial on linear algebra
- solution-tutorial on least squares
k-NN, Decision Trees, SVM and Kernel Trick