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"section": "About us",
"text": "About us\n\n\n\nDr. Katie Burak\nkburak@stat.ubc.ca\nhttps://katieburak.github.io/\nAssistant Professor of Teaching, Department of Statistics\n\n\n\n\n\nDr. Gabriela V. Cohen Freue\ngcohen@stat.ubc.ca\nhttps://gcohenfr.github.io/\nAssociate Professor, Department of Statistics\n\n\n\n\n\n\nUBC DSCI 200"
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- "title": "UBC DSCI 200",
- "section": "Wait, there’s two of you?",
- "text": "Wait, there’s two of you?\nGeoff & Trevor are co-teaching this course!\n\nThink of the two of us as interchangeable people.\n(It’s not that hard. We’re very similar.)\n\nWe will both be present at (almost) all lectures\nWe will roughly alternate who is giving the lecture\nWe are both in charge of course material / course policies / grades / etc."
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- "section": "Philosophy of the class",
- "text": "Philosophy of the class\nWe and the TAs are here to help you learn. Ask questions.\nWe encourage engagement and curiosity\nWe favour steady work through the term (vs. sleeping until finals)\nThe assessments attempt to reflect this ethos."
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- "section": "More philosophy",
- "text": "More philosophy\nWhen the term ends, we want\n\nYou to be better at coding.\nYou to have an understanding of the variety of methods available to do prediction and data analysis.\nYou to articulate their strengths and weaknesses.\nYou to be able to choose between different methods using your intuition and the data.\n\n\nWe do not want\n\nYou to be under undo stress\nYou to feel the need to cheat, plagiarize, or drop the course\nYou to feel treated unfairly."
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- "title": "UBC DSCI 200",
- "section": "Health/COVID Policies (TL; DR)",
- "text": "Health/COVID Policies (TL; DR)\n\nAttend class whenever you are healthy\nWe encourage you to wear a mask if you want\nDo NOT come to class if you are possibly sick\nThe Marking scheme is flexible enough to allow some missed classes"
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- "section": "What this course is",
- "text": "What this course is\n\n5 easy steps to use scikit-learn\nEverything there is to know about machine learning\nThe hypeist new machine leraning models\nThe fundamentals for developing strong intuitions/understanding about ML"
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- "section": "Predictive models",
- "text": "Predictive models\n\n1. Preprocessing\ncentering / scaling / factors-to-dummies / basis expansion / missing values / dimension reduction / discretization / transformations\n2. Model fitting\nWhich box do you use?\n3. Prediction\nRepeat all the preprocessing on new data. But be careful.\n4. Postprocessing, interpretation, and evaluation\n\nWe will focus mostly on 1 and 4."
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- "text": "Source: https://vas3k.com/blog/machine_learning/"
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- "text": "6 modules\n\n\n\nReview (today and next week)\nModel accuracy and selection\nRegularization, smoothing, trees\nClassifiers\nModern techniques (classification and regression)\nUnsupervised learning\n\n\n\n\nEach module is approximately 2 weeks long\nEach module is based on a collection of readings and lectures\nEach module (except the review) has a homework assignment"
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- "text": "Assessments\nEffort-based\nTotal across three components: 65 points, any way you want\n\nLabs, up to 20 points (2 each)\nAssignments, up to 50 points (10 each)\nClickers, up to 10 points\n\neffort_grade = max(65, labs + assignments + clickers)\n\nKnowledge-based\nFinal Exam, 35 points"
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- "section": "Labs / Assignments",
- "text": "Labs / Assignments\nThe goal is to “Do the work”\n\n\nAssignments\n\nNot easy, especially the first 2, especially if you are unfamiliar with R / Rmarkdown / ggplot\nYou may revise to raise your score to 7/10, see Syllabus. Only if you lose 3+ for content (penalties can’t be redeemed).\nDon’t leave these for the last minute\n\n\n\nLabs\n\nLabs should give you practice, allow for questions with the TAs.\nThey are due at 2300 on the day of your lab, lightly graded.\nYou may do them at home, but you must submit individually (in lab, you may share submission)\nLabs are lightly graded"
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- "text": "Clickers\n\nQuestions are similar to the Final\n0 points for skipping, 2 points for trying, 4 points for correct\n\nAverage of 3 = 10 points (the max)\nAverage of 2 = 5 points\nAverage of 1 = 0 points\ntotal = max(0, min(5 * points / N - 5, 10))\n\nBe sure to sync your device in Canvas.\n\n\n\n\n\n\n\nDon’t do this!\n\n\nAverage < 1 drops your Final Mark 1 letter grade.\nA- becomes B-, C+ becomes D."
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- "section": "Final Exam",
- "text": "Final Exam\n\nScheduled by the university.\nIt is hard\nThe median last year was 50% \\(\\Rightarrow\\) A-\n\nPhilosophy:\n\nIf you put in the effort, you’re guaranteed a C+.\nBut to get an A+, you should really deeply understand the material.\n\nNo penalty for skipping the final.\nIf you’re cool with C+ and hate tests, then that’s fine."
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- "section": "Late policy",
- "text": "Late policy\nIf you have not submitted your lab/assignment by the time grading starts, you will get a 0.\n\n\n\n\n\n\n\n\n\nWhen you submit\nLikelihood that your submission gets a 0\n\n\n\n\nBefore 11pm on due date (i.e. on time)\n0%\n\n\n11:01pm on due date\n0.01%\n\n\n9am after due date\n50%\n\n\n2 weeks after due date\n99.99999999%"
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- "section": "Late policy",
- "text": "Late policy\nWe will only make exceptions when you have grounds for academic consession. (See the UBC policy.)\n\n\n\n\n\n\nTip\n\n\nRemember: you can still get a “perfect” effort grade even if you get a 0 on one assignment."
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- "section": "Time expectations per week:",
- "text": "Time expectations per week:\n\nComing to class – 3 hours\nReading the book – 1 hour\nLabs – 1 hour\nHomework – 4 hours\nStudy / thinking / playing – 1 hour\n\n\nQuestions?"
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- "text": "Textbooks\n\n\n\n\n\n\nAn Introduction to Statistical Learning\n\n\nJames, Witten, Hastie, Tibshirani, 2013, Springer, New York. (denoted [ISLR])\nAvailable free online: http://statlearning.com/\n\n\n\n\n\n\n\n\n\nThe Elements of Statistical Learning\n\n\nHastie, Tibshirani, Friedman, 2009, Second Edition, Springer, New York. (denoted [ESL])\nAlso available free online: https://web.stanford.edu/~hastie/ElemStatLearn/\n\n\n\n\nIt’s worth your time to read.\nIf you need more practice, read the Worksheets."
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- "text": "Computer\n\n\n\n\n\n\nWe will use R and we assume some background knowledge.\nSuggest you use RStudio IDE\nSee https://ubc-stat.github.io/stat-406/ for what you need to install for the whole term.\nLinks to useful supplementary resources are available on the website.\n\n\n\n\n\n\nThis course is not an intro to R / python / MongoDB / SQL."
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- "text": "Other resources\n\nCanvas (minimal)\n\nQuiz 0, grades, course time/location info, links to videos from class\n\nCourse website\n\nAll the material (slides, extra worksheets) https://ubc-stat.github.io/stat-406\n\nSlack\n\nDiscussion board, questions\n\nGithub\n\nHomework / Lab submission\n\n\n\n\n\nAll lectures will be recorded and posted\nWe cannot guarantee that they will all work properly (sometimes we mess it up)"
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- "section": "Some more words",
- "text": "Some more words\n\nLectures are hard. It’s 8am, everyone’s tired.\nCoding is hard. We hope you’ll get better at it.\nWe strongly urge you to get up at the same time everyday. It’s really hard to sleep in until 10 on MWF and make class at 8 on T/Th.\n\n\n\nWe have to give you a grade, but we want that grade to reflect your learning and effort, not other junk.\n\n\n\nIf you need help, please ask."
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- "title": "UBC DSCI 200",
- "section": "Some things to see on the website",
- "text": "Some things to see on the website\n\nRead the syllabus (See also Lab 0)\nLinks to slides, how to download / print, browse source code\nInstall the R package, read documentation, check your LaTeX installation\nBE SURE to follow the Computer Setup instructions!\nWorksheets for extra help.\nRead the FAQ!\nView the Course GitHub (once you have access)\n\n\n\n\nUBC DSCI 200"
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