STAT318 — Data Mining Dr University of Canterbury, Christchurch, Some of the figures in this presentation are taken from “An Introduction to Statistical Learning, with applications in R” (Springer, 2013) with permission from the authors: G. James, D. Witten, T. Hastie and R. Tibshirani. , University of Canterbury 2021 STAT318 — Data Mining ,1 / […]
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End-of-year Examinations, 2019 STAT318 / STAT462-19S2 (C) Family Name First Name Student Number Venue Seat Number _____________________ _____________________ |__|__|__|__|__|__|__|__| ____________________ ________ No electronic/communication devices are permitted. No exam materials may be removed from the exam room. Mathematics and Statistics EXAMINATION End-of-year Examinations, 2019 STAT318 / STAT462 -19S2 (C) Data Mining Examination Duration: 120 minutes Exam
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APS1070 Foundations of Data Analytics and Machine Learning Fall 2021 Week 3: • End-to-endMachineLearning • Data Retrieval and Preparation • Plotting and Visualization • MakingPredictions • Decision Trees Prof. Agenda ➢Today’s focus is on Foundations of Learning 1. End-to-end machine learning 2. Python Libraries —NumPy —Matlplotlib —Pandas —Scikit-Learn 3. Decision Trees 2 Part 1 End-to-End
L3 APS1070 Week 3 Lecture Code¶ Data Exploration¶ It is strongly suggested that you follow along and run your own code during the lecture. By the end of this lecture, you should be able to: Setup and use Google Colab. Be able to perform basic operations using NumPy. Be able to plot using matplotlib. Be
Mid-year Examinations, 2019 STAT318-19S1 (C) / STAT462-19S1 (C) Family Name First Name Student Number Venue Seat Number _____________________ _____________________ |__|__|__|__|__|__|__|__| ____________________ ________ No electronic/communication devices are permitted. No exam materials may be removed from the exam room. Mathematics and Statistics EXAMINATION Mid-year Examinations, 2019 STAT318-19S1 (C) Data Mining STAT462-19S1 (C) Data Mining Examination Duration: 120
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APS1070 Foundations of Data Analytics and Machine Learning Fall 2021 Lecture 1: • Introduction • CourseOverview • Machine Learning Overview • K-nearestNeighbourClassifier Prof. 1 2 Instruction Team Instructor: Prof. Head-TA: Zadeh TA: TA: Haoyan (Max) A: TA: Get to know the instruction team: https://q.utoronto.ca/courses/223861/pages/course-contacts 3 Communication ➢Preferred contact method for a quick response: Piazza; 1.
MAST90138 Week 10 Lab Problems: 1. Recall the iris data which contain various measurements (sepal length, sepal width, petal length and petal width) of 50 flowers from each of 3 species of iris flowers (this it contains 150 ¡°individuals¡±). Load the iris data in R by typing data(iris). (a) Create from those data two samples
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APS1070 Foundations of Data Analytics and Machine Learning Fall 2021 Week 4: • Clustering • Probability Theory • SummaryStatistics • Gaussian Distribution • Performance Metrics Prof. Slide Attribution These slides contain materials from various sources. Special thanks to the following authors: • Sinisa Colic • • • Zadeh • Quick review! Algorithm ➢For a single
Time allowed: TWO HOURS Number of Pages: 37 Read these instructions carefully. • Answer all FOUR questions. • All questions carry equal marks. • Calculators are permitted. • Use black or blue ink only. • Show all working. • Write your answers in the spaces pro- vided. • You may use the left-hand pages for
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Computer Vision (7CCSMCVI / 6CCS3COV) Recap • Image formation ● Low-level vision ● Mid-level vision ● High-level vision ● Artificial – template matching – sliding window – edge matching – model-based – intensity histograms – implicit shape model – SIFT feature matching – bag-of-words – geometric invariants ● Biological Computer Vision / High-Level Vision /
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