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This course was new in 2018. It was then, and still is, inevitably somewhat experimental. It is also addressed to a rather heterogeneous audience, as it bears two numbers, one for Honours courses, the other for PhD courses. This means that I cannot tell with any certainty just how much work we will be able to cover.
The main text for the course is a book by Aurélien Géron, which I have found to be very useful for learning how to program machine-learning algorithms. The book was originally called Hands-on Machine Learning with SciKit-Learn and TensorFlow , and it is available from the O'Reilly website, to which McGill people can get free access. However, the book has been updated, with the new title Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition . Keras is a software layer that makes programming an algorithm easier than with TensorFlow, the leading platform until a few years ago.
Most of my own recent programming projects have been with the C++ programming language, but, unless one has quite a lot of prior experience with C++, it has a steep learning curve. In the last few years, I have been working with Python, an interpreted language that has, for the most part, a straightforward syntax, and can be learnt swiftly by anyone with even just a little experience of programming.
The relative simplicity of programming in Python is probably the main reason for which Python has quite the best set of libraries for machine learning, and not just for deep learning. Although deep learning will be the main focus of the course, I plan to look at some other machine-learning techniques, for which the Python libraries are equally useful. The study of some of these other techniques reveals how much all modern machine-learning approaches have in common, despite the fact that some are much better adapted than others for specific applications.
The second text for the course is Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. It is now aging gracefully, and so it has been demoted from the being the main text to being a supplementary text.
This link takes you to the website for the book. Its contents are completely available online. The book is also available in hardcover . It is published by the MIT Press.
The book contains three main sections. The first, Applied Math and Machine Learning Basics , provides necessary background material. It is the final chapter of this section that first discusses machine learning explicitly, and this will be our starting point. It may be necessary, at least for some students, to consult some of the earlier chapters on mathematical prerequisites.
Once we have got a good idea of the basics, we will go on to the study of the modern practices, following the material, or most of it, in Géron's book. This study necessarily involves work with the computer. Machine Learning cannot be understood in any useful way unless at least some of the different ways of training an algorithm have been implemented by each student. Some, but I hope not all, students may still be suffering from the analogue of math anxiety regarding computer programming, and I will try to be helpful in getting people over this nowadays unnecessary anxiety.
Provisionally, here is a summary of the topics I covered over the last few years, and hope to be able to cover this year as well, plus one addition, the last in the list.
There is a super-abundance of resources available online for studying machine learning, and for implementing it. Machine learning is often coupled with the buzzword Big Data, and this is simply because machines usually learn better if they have a lot of data available to train their algorithms. Many big datasets are available online, the best known, and probably the most comprehensive, being
https://www.kaggle.com/datasetsHere are some of the available resources which I found useful. First, some books that may or may not be available free on the Internet.
perceptrons.
As each year since I began to teach this course, I will consult you all before committing myself definitively to the method of evaluation. What I want is for each student to undertake the training of an algorithm, preferably but not necessarily using deep learning, on some (reasonably) big dataset. Although I say each student, it is also possible for students to work in a team of two. If some of you are still a little computer-shy, you might find it valuable to team up with another student who is more experienced with computers. The results of the work should then be written up, along with the relevant source code, and submitted, along with the results, as the document on which a final mark could be based. One way in which such a document could be created is to use a Jupyter notebook; go to
for information on what a Jupyter notebook is, and how to work with one.
Unless there is strong opposition, I plan also to set a couple of assignments that will carry some weight in the final grade, but will not be allowed to lower the grade based solely on the big project. The idea behind the assignments is just to give you practice in machine learning and confidence in carrying it out.
Another possibility would be a formal exam, but that would be contrary to the spirit of the course. While some weight could perhaps be given to students' ability to answer theoretical questions, or to carry out some mathematical operations successfully, I wouldn't want this weight to be very great.
The university now wants all course outlines to specify rules concerning the use of generative AI for a course. As I explained in class, you are welcome to use AI for help with assignments or take-home exams. The only qualification is that each such use must be acknowledged, just as sources are acknowledged in any research paper. You may use any suitable format for the acknowledgement, but please do acknowledge all uses you may make of generative AI, such as Chat-GPT for instance.
You'll have seen the following in all of your course outlines, because the McGill Senate requires that it should appear in all of them. I used to think of it as a pure formality, but a disturbing number of cases of plagiarism have been detected in recent years. So, please take seriously all the admonitions in the following text.