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CSEP 546 Machine Learning

CSEP 546 Machine Learning. Lecturer: Geoff Hulten TAs: Alon Milchgrub , Andrew Wei. Logistics. Course website: https://courses.cs.washington.edu/courses/csep546/19au/ Canvas site: https://canvas.uw.edu/courses/1331659

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CSEP 546 Machine Learning

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  1. CSEP 546Machine Learning Lecturer: Geoff Hulten TAs: Alon Milchgrub, Andrew Wei

  2. Logistics • Course website: https://courses.cs.washington.edu/courses/csep546/19au/ • Canvas site: https://canvas.uw.edu/courses/1331659 • Discussion board: http://piazza.com/washington/fall2019/csep546/home/

  3. Introducing Myself • Geoff Hulten • ghulten@cs.washington.edu • https://www.linkedin.com/in/geoff-hulten-58136a1/ • What I’ve worked on… • Why I’m here…

  4. Introducing our TAs • Alon Milchgrub • alonmil@cs.washington.edu • Quick Intro • Office hours: • Tues: 5:30 – 6:30 CSE 674 • Andrew Wei • nowei@cs.washington.edu • Quick Intro • Office Hours: • Tues: 4:30 – 6:20 CSE 007 Virtual hours for people viewing from MS? Mail me.

  5. Introducing the Class What types of jobs? • Engineering? • Data science? • Management/PM? • Other? Machine learning experience? • This is my first class? • Several classes / exploration? • Do it for a living? Math? • Not really? • Can do? • I think in math? Python? • Never used? • Some experience? • No problem?

  6. Overview of the Course • Learn important machine learning algorithms (the tools) 2) Learn how to produce models (use the tools) 3) Learn how to produce working systems (ML Engineering)

  7. Lecture Overview Subject to change

  8. Assignments • Logistic Regression • Feature Engineering (text) • Decision Trees • Ensembles (Random Forests) • Clustering & Instance Based • Feature Engineering (Vision) • Neural Networks • Reinforcement Learning • Model building & interpreting • And several Kaggle style competitions

  9. Evaluation Assignments: There will be reading assignments every week (~10% of grade) and coding / modeling assignments most weeks (~40% of final grade) and three Kaggle style competitions (~25% of final grade). Assignments due two weeks after they are assigned. Except (possibly) for the last assignment, which is due before the start of the final lecture (so we can submit final course grades in a timely fashion). Clarity of communication is critical in machine learning, so your answers must be concise and easy to follow. If the TA can’t evaluate the answers in reasonable time they will have to give reduced credit. Exam: There will be an exam worth ~25% of the final grade (although you must score at least 50% on the exam to pass the course). This will be online and timed (2 – 3 hours). Tentatively scheduled for Dec 9th (complete it any time before midnight) This exam will be based on the assignments, readings, and lectures.

  10. The Textbooks and why… All royalties to be donated…

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