If you are struggling with the material, the new hybrid teaching format, or anything else, please reach out. Our goal is to help each of you succeed in the course. If you need extra support, the teaching team is here to work with you. If you are unable to submit a deliverable on time, please reach out before the deliverable is due. I hope you will extend me the same grace! If you tell me you are having trouble, I am not going to judge you or think less of you. In general, everyone is struggling to some extent. We are working on this course during the global pandemic. There will be at least a few office hours which will be held online. Most of the class activity will be video recorded and will be made available to you. (But you need to be present in the classroom to write the exam unless there is a legitimate reason for not doing so.) In this class, the marking scheme is intended to provide flexibility so that you can prioritize your health and still be able to succeed:Īll course notes will be provided online.Īll homework assignments can be done and handed in online.Īll exams will be held online. Your precautions will help reduce risk and keep everyone safer. You can check this website to find out if you should self-isolate or self-monitor. Stay home if you have recently tested positive for COVID-19 or are required to quarantine. If you are ill or believe you have COVID-19 symptoms or been exposed to SARS-CoV-2 use the Thrive Health self-assessment tool for guidance, or download the BC COVID-19 Support App for iOS or Android device and follow the instructions provided. UBC no longer requires students, faculty and staff to wear non-medical masks, but continues to recommend that masks be worn in indoor public spaces. Masks: This class is going to be in person. Calling BS videos Chapter 6 (6 short videos, 47 min total).Calling BS videos Chapter 5 (6 short videos, 50 min total).□ (Optional but highly recommended) Calling Bullshit 4.1: Right Censoring (Optional) Humour: The Problem with Time & Timezones More depth on metrics less depth on regressionįeature importances, model interpretationįeature importances is new, feature engineering is newįeature engineering and feature selection Hyperparameter optimization, overfitting the validation set More preprocessing, sklearn ColumnTransformer, text features Once they are finalized, I’ll post them in the Course Jupyter book. So if you check them before the lecture, they might be in a draft form. I’ll be developing lecture notes directly in this repository. During the lecture, I’ll summarize the important points from the videos and focus on demos, iClickers, and Q&A. Try to watch the assigned videos before the corresponding lecture. All the videos are available on YouTube and are posted in the schedule below. There will be pre-watch videos for many lectures, at least in the first half of the course. This course will be run in a semi flipped classroom format. You will get a lot more out of the course if you show up in person. That said, consider the recordings a backup resource and do not completely rely on it. You can find the link of Panopto videos in Canvas. Live lectures: The lectures will be in-person in DMP 310 from 11am to 12:20pm, as marked in the Calendar. For this course, we’ll assume that the Calendar is always right! If you find inconsistencies in due dates, follow the due date in the Calendar. I’ll also add the due dates in the Calendar. Usually the homework assignments will be due on Mondays (except next week) and will be released on Tuesdays. Important links #Ĭanvas: You will find Gradescope, Piazza, and Panopto links in Canvas See the license file for more information. Software licensed under the MIT License, non-software content licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License. You are looking at the current version (Sep-Dec 2022). This is the course homepage for CPSC 330: Applied Machine Learning at the University of British Columbia. UBC CPSC 330: Applied Machine Learning (2022W1) # Lecture 19: Multi-class classification and introduction to computer vision Lecture 18: Introduction to natural language processing Lecture 16: DBSCAN and Hierarchical Clustering Lecture 13: Feature engineering and feature selection Lecture 10: Regression Evaluation Metrics Lecture 8: Hyperparameter Optimization and Optimization Bias Lecture 2: Terminology, Baselines, Decision Trees
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