You can obtain the course material slides, code examples, etc. However, note that links to these materials will always be shared on the Canvas Pages, so you do not check different websites separately. If there are problems with viewing or obtaining these files for example, because of restricted internet access , please let me know — I am happy to find alternative solutions then, such as uploading the material to Google Drive or the internal Canvas storage if possible.
Important information and announcements : Important course information and deadlines as well as updates or changes will be shared via the Announcements on Canvas. You should get an automated email each time I upload a new announcement there, but it does not hurt to check the Announcements page manually every day to make sure you did not miss any important information.
Submissions : Homework assignment submissions and project submissions are to be submitted via the Canvas Assignmens function.
I will provide more information and instructions regarding submissions throughout the semester. Questions : The best place for asking questions is the Piazza forum I set up for this course.
Asking questions via Piazza instead of using email is most efficient in case multiple students have the same or similar questions. Students are also encouraged to help other students on Piazza. However, for personal questions missed assignments etc.
I will link resources, including internet articles and research articles that are relevant for the course. The book suggestions are recommendations but not requirements. Regarding Python, we will mainly focus on two libraries: NumPy and Scikit-learn. You can think of NumPy as a linear algebra library that provides utilities similar to MatLab if you are familiar with MatLab. Scikit-learn is the main machine learning library we will be using.
However, some basic familiarity with Python will be necessary in order to use these libraries. To make the grading more transparent and provide students with a better intuition of their performance throughout the course, there will be a total of points in this course. However, due to the COVID context and the change to all-online instructions and exams, grades may be curved to adjust for differences in online teaching compared to previous in-person semesters.
The exams will take place online through Canvas during specific times:. The final will be cumulative in the sense that some of the earlier topics may be relevant to the final exam; however, the final exam will largely focus on the parts covered after the midterm. In other words, you still should be familiar with all concepts covered in the course, but questions will be centered around the topics after the midterm.
Q: Does the computational time complexity of a k-Nearest Neighbor classifier grow linearly, quadratically, or exponentially with the number of samples in the training dataset?
Explain your answer in sentences. Answer: Linearly. For each new training point there is an additional distance computation. The goal of working on a class project is three-fold. First, it will provide you with the opportunity to apply the concepts learned in this class creatively, which helps you with understanding material more deeply.
Third, along with the opportunity to practice and the satisfaction of working creatively, students can use this project to enhance their portfolio or resume for example, by sharing it publicly on your GitHub account or personal website — this is optional. The project proposal is not graded by how exciting your project is but based on whether you follow the objectives of the project proposal, project presentation, and project report. Again, the objective of this project is to provide you with hands-on practice and an opportunity to learn.
Please note that you should use the proposal-latex file s for writing and submitting your proposal! Also, the project proposal offers a chance to receive useful feedback and suggestions on your project. For this project, you will be working in a team consisting of three students. You are encouraged to form groups by yourself, as discussed in class.
If you cannot find group members, the TA and I will randomly assign you to a group. If you have any concerns working with someone in your group, please talk to a TA or the instructor for accommodations. You are expected to share the workload evenly, and every group member is expected to participate in both the experiments and writing. As a group, you only need to submit one proposal and one report, though. So you need to work together and coordinate your efforts. It is crucial that you talk to each other regularly!!!
After you have received feedback from me and your project proposal has been graded, you are advised to stick to the project outline in the proposal as closely as possible. If you wish to update your project outline, talk to me or the TA first. For each section, you can receive a maximum of 10 points, totaling 50 pts for the proposal overall. Even minor forms of plagiarism e. And university guidelines dictate that severe incidents need to be reported. During the last three lectures, you will be presenting your project to the class.
The presentation should be minutes long. Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples.
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