Cpsc 340 ubc 2018. Admin •Midterm is tomorrow.

Cpsc 340 ubc 2018 2 Methods based on averaging and integration (Bayesian learning). Studying CPSC 340 Machine Learning And Data Mining at The University of British Columbia? UBC; Machine Learning And Data Mining; Machine Learning And Data Mining (CPSC 340) 9 9 documents. Course Info. –Assuming there isnt a class here at 5pm. Naïve Bayes • Decision trees: 1. Admin •Assignment 2 is due tonight. One of the aspects of the course that was a bit annoying was the webwork. Write better code with AI CPSC 340 vs. Bayes rule and maximum expected utility. •When k > 1, have scaling, rotation, and label switching. Least Squares Cost •Cost of solving normal equations XTXw = XTy? •Forming XTy vector costs O(nd). Last Time: Classification using Regression CPSC 340: Machine Learning and Data Mining Principal Component Analysis Fall 2017. A Simple Setting: Coupon Collecting •Assume we have a categorical variable with 50 possible values: –{Alabama, Alaska, Arizona, Arkansas,}. Admin •Assignment 4: January 24, 2018 1 Thinking about Optimization CPSC 340: Machine Learning and Data Mining The University of British Columbia 2017 Winter Term 2 Mike Gelbart In [1]: import numpy as np Machine Learning and Data Mining undergraduate course taught by Dr. 3) Collect data. Every year one or two courses are cancelled for lack of No Free Lunch Theorem •Let [s show the no free lunch theorem in a simple setting: –The x iand yi are binary, and y being a deterministic function of xi. –I wont be offended if you leave early. And yes there is a lot of overlap, regression is seen in Stat 300 and Cpsc 340 while almost all of Stat MATH 223 (Honours Linear Algebra, 2018-19) CPSC536J (Topics in Algorithms and Complexity: Linear Algebra Problems, 2018-19) MATH 441 (Math Modeling: Discrete Optimization Motivation: Pixels vs. Admin •Welcome to the course! –If you have remaining forms, bring them to me after class and good luck. Admin •Midterm is tomorrow. Last Few Lectures: Latent-Factor Models •Weve been discussing latent-factor models of the form: •We get different models under different conditions: –K-means: each z i has one 1 and the rest are zero. Models of algorithms for dimensionality reduction, nonlinear regression, classification, clustering and unsupervised learning; applications to computer graphics, computer games, bio-informatics, information retrieval, e-commerce, databases, computer vision and artificial intelligence. –Data points = objects = examples= rows = observations. Class Schedule. CPSC 340: Machine Learning and Data Mining Even More Deep Learning Fall 2018 CPSC 340: Machine Learning and Data Mining Regularization Fall 2018 For CPSC 320 I would recommend touching up on algorithms and data structures you learned in CPSC 221. Submenu. –x ij is feature j for example Zi (quantity of food j on day Zi). 5) Transform data or select useful subsets. –It has d elements, and each is an inner product between n numbers. Slide 28. L2-Regularization Sparse ‘w’ (SelectsFeatures) Speed Unique ‘w’ CodingEffort Irrelevant Features L0-Regularization Yes Slow No Few lines Not Sensitive L1-Regularization Yes* Fast* No 1 line* Not Sensitive I'll have you know I graduated top of my class in CPSC 340, and I've been involved in numerous secret model generations with Frank Wood, and I have over 300 confirmed classifiers. Fri Sep 14. Slides for this lecture at https://github. com/UBC-CS/cpsc340/ Motivation: Pixels vs. •This reading group covers topics that arent yet in these course. Motivating Problem: Depth Estimation from Images We want to build system that predicts \distance to car" for each pixel in an image: Overview of CPSC 340 topics: you are expected to know all this already. •Friday [s lectures: –Mike will do a course review in his section. Solution of linear This entry was posted in Course Reviews and tagged decision theory, game theory, phil 321, stephens, ubc on April 26, 2018 by arman raina. Save. Term. You can click on each course for additional information, and to see if the course is delivered during the current semester. •Decision trees (greedy recursive splitting using decision stumps). MWF 12-1p (Term 1), 4-5p (Term 1), 1-2p (Term 2) Website. Admin •You can submit A1 with late day on Monday night. Related Courses: Besides CPSC 340, other closely-related courses available at UBC include EECE 360, EECE 592, EOSC 510, STAT 305/306/406, STAT 460/461/560/561, STAT 540, and CPSC 532P. pdf. 1. •Final: –Next Tuesday, details and previous exams posted on Piazza. Probability. TA: Hamed Shirzad. Parts •Can view 28x28 image as weighted sum of single pixel on images: –We have one image/feature for each pixel. Sign in Product GitHub Copilot. Lecture info: Tuesdays/Thursdays, 15:30 - 16:50, Swing 210. Related Courses: Besides CPSC 340, other closely-related courses available at UBC include 500-level classes taught by Frank Wood, Leonid Sigal, and Helge Rhodin. Lecture info: Mondays/Wednesdays, 13:30 - 15:00, DMP 101 (also available on Zoom). CPSC 340: Machine Learning and Data Mining Non-Parametric Models Fall 2022. •Mark’s office hours will be cancelled on Tuesday (since he’s away). Office hours: Mondays 12-1pm, Thurdsays 4-5pm (ICICS X563 or come to the class Zoom). CPSC 322, Lecture 1 Slide 27 • Machine learning , ML and AI are not the same thing • This course will not cover ML; for that, you want . Course Page. Last Time: MAP Estimation •MAP estimation maximizes posterior: 1/22/2018 1:05:00 PM CPSC/Stats combined major here. Key Concepts. None. •Linear regression makes predictions ො i using a linear function of x i: •The parameter w is the weight or regression coefficient of x CPSC 340: Machine Learning and Data Mining Multi-Dimensional Scaling Fall 2018. Prospective Undergrads. Introduction. Both CPSC 303: Numerical Approximation and Discretization 2017/2018 Winter Term 2 (January-April 2018) Mon/Wed/Fri 14:00-15:00, DMP 110. Computer Science and Statistics at UBC. Camera operated by Tanner Johnson. –Can view exam during Mike or my office hours this week and next week. It just felt like a summer break where I had to go to UBC twice a week CPSC 340: Machine Learning and Data Mining More Deep Learning Fall 2018. –Make sure that answers are in the PDF where requested. Linear prediction. non -parametric, ensemble methods. Tree 4. Note that your nal and midterm groups will not be allowed to have any overlap in CPSC 340: Machine Learning and Data Mining taught at the University of British Columbia in 2018. However, the solution file is meant for you alone and we do not. Last Time: Classification using Regression Deep Learning “Tricks of the Trade” • We’ve discussed heuristics to make deep learning work: – Parameter initialization and data transformations. Time (start) 12:00 PM. –Last names starting with A-L: BUCH A102. There are some exceptions: 1 Methods based on counting and distances (KNN, random forests). give permission to share these solution files with anyone. –Last names starting with M-Z: BUCH A104. CPSC 340: Machine Learning and Data Mining Finding Similar Items Fall 2018. UBC CPSC 340 / CPSC 532M 2018W1 FINAL EXAM December 13th, 2018 Instructors: Mark It has a column named 'vs CPSC 340' which basically outlines the differences between CPSC 330 and 340. –x i is the list of all features for example Zi (all the quantities on day i). Section. In This Lecture • Random Forest (15 minutes) • Unsupervised Learning Intro (15 minutes) • K-Means Clustering (15 minutes) 2. Admin • Assignment 5 due Friday at 11:55pm – Extended by 12+ hours – We will cover all relevant details today • This lecture will be 75 minutes long • Office hours and tutorials had low attendance. Variables + Constraints. Time (end) 1:00 PM. Later in course. Admin •Midtermon Feb 14 in class. –It has d2 elements, and each is a sum of n numbers. –Extra time wont be testable. ca, ICICS X539. CPSC 340 101 2021W Instructor(s) Danica Sutherland. Only students who have (or will have) completed CPSC 340 or CPSC 532M or an equivalent rigorous introduction to Machine Learning (not just CPSC 340/532M vs. UBC Canvas: Announcements, grades, quizzes, lecture notes, homework assignments, sample exams, solutions, additional files, surveys; Winter 2018. Classification. After each lecture, you can download the video or watch it in youtube, where it is listed as undergraduate machine learning. Ultimately the average for the course was an A and I don’t think there was any scaling. https://www. Th Jan 17. The prof has been telling us that it is a very difficult course and we probably should have taken cpsc320, UBC Schedule Optimizer: get best schedules by walking times, gaps, prof ratings, and more CPSC 340: Machine Learning and Data Mining More Linear Classifiers Fall 2018. –October 18th at 6:30pm (BUCH A102 and A104). The book is available online to UBC CPSC_V 302 (3) Numerical Computation for Algebraic Problems Numerical techniques for basic mathematical processes involving no discretization, and their analysis. The land it is situated on has always been a place of CPSC 330 is broader, and includes topics like data cleaning and communicating your results; CPSC 340 goes deeper into the algorithms and the mathematical / numerical considerations CPSC 340: Machine Learning and Data Mining PCA: the model (“predict”) Original version of these slides by Mark Schmidt, with modifications by Mike Gelbart. Problem: Deterministic; Stochastic. The class is intended as a continuation CPSC 340 (which is also known as 532M for grad students) and it is strongly recommended that you take CPSC 340 or 532M first before enrolling in CPSC 540. CPSC 340: Machine Learning and Data Mining Fun Examples (Bonus Lecture) Summer 2021. •Assignment 2 is out. Post navigation ← Course Review: CPSC 340 Course Review: CPSC 303 → Sparsity and Non-Negativity •Similar to L1-regularization, non-negativity leads to sparsity. Related Courses: Besides CPSC 340, other closely-related courses available at UBC include 500-level classes taught by Frank Wood, Leonid Sigal, Helge Rhodin, Kwang Moo Yi, Danica Sutherland, Jeff Clune, and Mijung Park. Dates. CPSC 340; 01: May 13: Course intro: 📹 ; Tag Archives: cpsc 340 This entry was posted in Course Reviews and tagged course review, cpsc 340, machine learning, mike gelbart, ubc on April 27, 2018 by arman raina. •But no instructor office hours this week (Mark is UBC CPSC 340 2019W MIDTERM EXAM Oct 17th, 2018 Instructor: Mark Schmidt TIME: 80 minutes. 2018 (UBC CS) Sci-fi/fantasy fan. CPSC 340/532M vs. Course Question Grad students take it under 532M, so their scores don't impact the 340 average (listed as different courses, so different averages). CPSC 320 2017W2: Assignment 1 January 13, 2018 Please submit this assignment via GradeScope at https://gradescope. –Closed CPSC 340/532M and CPSC 540 •There are two ML classes: CPSC 340/532M and 540. CPSC 440 (cross-listed as CPSC 545 for graduate students) focuses on probabilistic methods which appear in more niche applications, as well as various other topics not covered in 340/540. The lectures for 340, the undergrad version of this course, are in youtube: undergraduate machine learning. •Assignment 5: –Will be released soon, maybe Tuesday. Login The University of British Columbia Digression: Example of Optimization Bias •Consider a multiple-choice (a,b,c,d) “test” with 10 questions: –If you choose answers randomly, expected grade is 25% (no bias). Don't leave it till the last minute. Jeff Clune. testing, parametric vs. CPSC 540 is *not* an introductory course. Before taking 340, review linear algebra, probability and multivariate calculus. The book is available online to UBC students from the UBC library. 201. Tue Jan 8. Last year 410 was cancelled. –More prerequisites and higher workload. Models of algorithms for dimensionality reduction, nonlinear regression, classification, clustering and unsupervised learning; applications to computer graphics, CPSC 340: Machine Learning and Data Mining Kernel Trick Original version of these slides by Mark Schmidt, with modifications by Mike Gelbart. –Structured as one full-year course: 440 starts where 340 ends. Why Study Optimization in CPSC 540? In machine learning,training is typically written as optimization: We numerically optimize parameters wof model, given data. Jan 8, 2017 CPSC 340: Machine Learning and Data Mining taught at the University of British Columbia during January-April 2018. Post navigation ← Course Review: CPSC 340 Course Review: CPSC 303 → Undergraduate students should enroll in CPSC 340 while graduate students should enroll in CPSC 532M (which has an extra small project component -- not offered in summers). RANDOM FORESTS Coming Up Next. ubc. For more details see class meetings on syllabus. –Previous midterms on course homepage. –xj is column j of the matrix(the value of feature j across all examples). Below are more details on CPSC 340/532M and CPSC 540 •There are two core ML classes : CPSC 340/532M and 540. 3 posted (and pinned) on Piazza. Don't get me wrong, I have a strong interest in software, I am a self-taught and have worked for a year as a software CPSC 340: Machine Learning and Data Mining taught at the University of British Columbia in 2018. If you have a strong grasp of that, it is a lot easier to learn the new ones. I'm signed up for CPSC 340 this summer with only the required prerequisites. Last Time: PCA with Orthogonal/Sequential Basis •When k = 1, PCA has a scaling problem. Or ask I’ve done just CPSC 121 during the summer and it was alright, but still tough. Date (start) Tue, Sep 4, 2018. –Covers implementation of methods based on counting and gradient descent. reReddit: Top posts of March 2018. We acknowledge that the UBC Vancouver campus is situated on the traditional, ancestral, and unceded territory of the xʷməθkʷəy̓əm (Musqueam). •Assignment 4: –Due Friday of next week. •Assignment 1 grades available? •Midterm rooms are now booked. Loss Scroll down to see the steps involved, but here is a 40-second video just as a reference as you work through the steps. •If k is small, we can use this to visualize high-dimensional data. CPSC 340: Data Mining Machine Learning Author: Mark William Schmidt ^Association Approach •A simple/common way to do feature selection: –For each feature j [, compute correlation between feature values xj and y [. A lot of the material was also reviewed from CPSC 340. ca/~schmidtm/Courses/340-F18 Studying CPSC 340 Machine Learning And Data Mining at The University of British Columbia? On Studocu you will find practice materials, mandatory assignments, lecture. Choose models that are widely-used in • ImageNet organizer visited UBC summer 2015. REGRESSION-VERSIONS OF CLASSIFIERS WE’VE COVERED Coming Up Next 3. •Assume each category has probability of 1/50 of being chosen: CPSC 340: Machine Learning and Data Mining Stochastic Gradient Fall 2017. Discrete Vairables Continuous Distributions Density Estimation and Fundamental Trade-o \Product of independent" distributions: CPSC 340: Machine Learning and Data Mining More Regularization Fall 2017. Basically, CPSC 340 is aimed at those who want to build a specialization in ML, Access study documents, get answers to your study questions, and connect with real tutors for CPSC 340 : at University of British Columbia. CPSC 440: Advanced Machine Learning (Winter 2022, Winter 2021, Winter 2020, Winter 2019, Winter 2018, Winter UBC’s Point Grey Campus is located on the traditional, ancestral, and unceded territory of the xwməθkwəy̓əm (Musqueam) peple. –Fundamental trade-off, no free We discussed unprecedented vision/speech performance. V6T 1Z4 CANADA Phone: 1-604-822-4368 Email 3 types of classi ers discussed in CPSC 340/540: Model \Classic ML" Structured Prediction Generative model p(y;x) Naive Bayes, GDA UGM (or MRF) Discriminative model p(yjx) Logistic regressionCRF Discriminant function y= f(x) SVM Structured SVM Discriminative models don’t need to model x. •Otherwise the TA can give 0 for the question. CPSC 340 202 2023W Instructor(s) Jeff Clune. Latent-Factor Models for Visualization •PCA takes features x i and gives k-dimensional approximation z i. CPSC 340: Machine Learning and Data Mining Ensemble Methods Fall 2018 CPSC 340: Machine Learning and Data Mining MAP Estimation Fall 2018. CPSC 340: Machine Learning and Data Mining More CNNs Fall 2018. CPSC 340 - Machine Learning and Data Mining (Fall 2017) Lectures and graduate students from any department are welcome to take the class. CPSC 340: Machine Learning and Data Mining K-Means Clustering Summer 2021. Motivation: Human vs. CPSC 540: CPSC 340 and CPSC 540 are roughly structured as one full-year course. 11/16/2018 6:36:40 PM Admin •Assignment 0is due Wednesday at 9pm (in 2 days) •Assignment 1 should be released Wednesday, due a week later –If you want to work with a partner, you both must request it BEFORE a1 release CPSC 302: Numerical Computation for Algebraic Problems 2017/2018 Winter Term 1 (September-December 2017) Mon/Wed/Fri 13:00-14:00, DMP 301. Admin •Assignment 1is out: –Due Wednesday. 1 Admin • Assignment 6: – This is the course webpage for the Machine Learning course CPSC 340 taught by Mark Schmidt in Fall 2015. . 2017/2018 None. •Midterm: –Grades posted. –Standard fix: use normalized orthogonal rows W This entry was posted in Course Reviews and tagged alan wagner, course review, cpsc 317, cpsc317, internet computing, networking, ubc on April 28, 2018 by arman raina. The land it is situated on has always been a place of learning for the Musqueam people, who for millennia have passed on their culture, history, and traditions from one generation to the next on this site. •Midterm: –You can view your exam during instructor office hours or after class Friday. –You can take CPSC 440 for grad credit as CPSC 540. 2018W. C. One is on machine learning, arguably the hottest subfield right now in computer science. Overview of CPSC 340 topics: you are expected to know all this already. CPSC 330 vs. reReddit: Top posts of 2018 PCA Computation: Alternating Minimization • With centered data, the PCA objective is: • This objective is not jointly convex in W and Z. Where: MCLD 2018; Who: Students form both sections are welcome to attend all office hours. Instructor: Danica Sutherland (she): dsuth@cs. CPSC 340 vs. Search for: Recent Posts. cpsc 340, stat 406, cpsc/math 40X) Share Sort by: Best. •Extra office hours: 1/22/2018 1:04:33 PM CPSC 340: Machine Learning and Data Mining MLE and MAP Original version of these slides by Mark Schmidt, with modifications by Mike Gelbart. Last Time: omputer Vision NN Revolution •CNNs are now being used beyond image classification: •Trend towards end-to-end systems: –Neural network does every step, backpropagation refines every step. CPSC 302: Numerical Computation for Algebraic Problems 2017/2018 Winter Term 1 (September-December 2017) Mon/Wed/Fri 13:00-14:00, DMP 301. CPSC 340 Final . Note: In the timetable below, the textbook codes (such as "AI:AMA") are CPSC 340 and 532M - Machine Learning and Data Mining (Fall 2022) Lectures Sections (beginning September 6): 12-1pm (Monday/Wednesday/Friday in UBC Life Building 2201) 4-5pm (Monday/Wednesday/Friday in UBC Life Buliding 2302) CPSC 340 101 2018W Instructor(s) Mark Schmidt. Various education programs and courses at UBC focus on machine learning and its applications. CPSC 340: Machine Learning and Data Mining Decision Trees Fall 2018 CPSC 340 202 2023W Instructor(s) Jeff Clune. Parameter initialization and data transformations. Teaching and Research Groups. •Label vector y [contains the labels of the examples. pyplot as plt %matplotlib inline 1. Looking for some advice or potential warning about CPSC 406 - Computation Optizamation, I'm a mechanical engineering student and this would be the first computer science course I've taken at UBC so I'm not really sure what to expect. CPSC 320 was moderately difficult, a harder than CPSC 221, but not too Linear Regression in 1 Dimension •Assume we only have 1 feature (d = 1): –E. For other students, to enroll in the course you need to sign up for the wait list. CPSC 340 and 532M - Machine Learning and Data Mining (Fall 2022) Lectures Sections (beginning September 6): 12-1pm (Monday/Wednesday/Friday in UBC Life Building 2201) 4-5pm (Monday/Wednesday/Friday in UBC Life Buliding 2302) January 24, 2018 1 Thinking about Optimization CPSC 340: Machine Learning and Data Mining The University of British Columbia 2017 Winter Term 2 Mike Gelbart In [1]: import numpy as np import matplotlib. In the next few classes we’ll focus ondensity estimation: UBC CPSC 340 2019W1 MIDTERM EXAM Oct 17th, 2018 Instructor: 2018W1final_questions. 0 0 questions. These are some of the key tools Synopsis: We introduce basic principles and techniques in the fields of data mining and machine learning. UBC Courses: CPSC 340 and 540: Machine Learning and Data Mining (Fall 2023, Fall 2022, Fall 2019, Fall 2018, Fall 2017, Fall 2016, Fall 2015). Last Time: •ImageNet organizer visited UBC summer 2015. 1/22/2018 1:03:35 PM CPSC 340: Machine Learning and Data Mining Semi-Supervised Learning Fall 2016. –The weights specify how much of this pixel is in It may not be a requirement, but it's still a 400 level cpsc course that should be available in both terms. Login; The University of British Columbia. 3 Tree 1 Tree 2. •Solving a d x d system of equations costs O(d3). Tree 5. Mon Sep 10. •Say that j is relevant if correlation is Aside: terminology woes •Different fields use different terminology and symbols. 4) Clean and preprocess the data. Random Forests CPSC_V 340 (3) Machine Learning and Data Mining. Post navigation ← Course Review: MATH 321 Course Review: CPSC 340 → Winter 2, 2018 CPSC 532S Winter 2, 2018 CPSC 425 Winter 1, 2018 CPSC 532L Winter 2, 2017 CMU 15-869 Fall 2012 CMU 16-824 Spring 2012 CSCD18 Fall 2008 CSCD18 Fall 2007 Publications | Code and Data | Contact Dep. As for the difficulty, I found CPSC 340 very easy, but I had a strong foundation for that. CPSC 340 (Machine Learning and Data Mining): students who are interested in machine learning will want to take CPSC 340. Jiarui Ding. 2017/2018. If you struggle with this math, the instructor Missing UBC student, Jasdeep Parmar. M. Maximum likelihood and linear prediction. –Inputs = predictors = features = explanatory Spring 2019 CPSC 340 39 173 39 — — — Fall 2018 CPSC 532/539W 39 21 28 — — “Deep Probabilistic Learning and Inference” Element. CPSC 340. Section vs. CPSC 320 was moderately difficult, a harder than CPSC 221, but not too End of Part 1: Key Concepts •We saw 3 ways of learning: –Searching for rules. We introduce basic principles and techniques in the fields of data mining and machine learning. Tue Jan 15. Syllabus. 1 View 2018W1final_questions. Or request another time; Hello! I will be starting my 3rd year at UBC in September, and I would appreciate if some CPSC 3rd Year & 4th Year students could provide their thoughts on my degree planning and answer some of my questions. Admin •Assignment 2 is due Friday. Winter 2018. Time (start) 4:00 PM. Admin •Assignment 5: –3 late days to hand in Friday. –Active learning (semi-supervised where you choose examples to label). CPSC 340 202 2022W Instructor(s) Andreas Lehrmann. •Assignment 1: –1 late day to hand in tonight, 2 for Friday. See also the Supplementary material including Matlab programs and UBC CPSC 330: Applied Machine Learning (2024s) Introduction. •esides huge dataset/model/cluster, what is the most important? _ 1. –Inputs = predictors = features= explanatory variables= regressors= independent variables = covariates = columns. •CPSC 340/532M: –Introductory course on data mining and ML. Clustering. Pst - Article. Tree 3. Deep Learning “Tricks of the Trade” • We’ve discussed heuristics to make deep learning work: – Parameter initialization and data transformations. CPSC 540 •There is also a more-advanced ML course, CPSC 540: –Starts where this course ends. • “Besides huge dataset/model/cluster, what is the most important?” 1. Present most of the fundamental ideas, sometimes in simplified ways. CPSC 340: Machine Learning and Data Mining Convolutional Neural Networks Fall 2017. 2013–2018) as well as my answers to some frequently asked questions regarding statistics, computer science and math courses. g. CPSC 340 Machine Learning and Data Mining Summer 2021. 2009 Midterm 1 (Summer) Midterm 2 (Summer) Midterm 3 (Summer) Sample Final (Summer) (Solution) Outline CPSC 101 CPSC 110 CPSC 121 CPSC 173 CPSC 210 CPSC 211 CPSC 213 CPSC 221 CPSC 302 CPSC 310 CPSC 311 CPSC 312 CPSC 313 CPSC 314 CPSC 320 CPSC 322 CPSC 340 CPSC 404 CPSC 422 CPSC 448B Uncategorized CPSC V 340 201 2024W Instructor(s) Jiarui Ding. an individual component 2. I'll have you know I graduated top of my class in CPSC 340, and I've been involved in numerous secret model generations with Frank Wood, and I have over 300 confirmed classifiers. •Mike and I will get a little out of sync over the next few lectures. Wed Sep 05. –Emphasis on applications and core ideas of ML. •532M Projects: –No news is good news: e CPSC 340: Machine Learning and Data Mining Recommender Systems Fall 2018. Admin •Assignment 4: –Should be posted tonight and due Friday of next week. •UBC connection (first paper on this topic): Global Distance-Based Outlier Detection: KNN CPSC 340: Machine Learning and Data Mining Gradient Descent Fall 2018. •Forming matrix XTX costs O(nd2). cs. A rough CPSC 322 overview. •Linear regression makes predictions ො i using a linear function of x i: •The parameter w is the weight or regression coefficient of x CPSC 340 vs. –Sparsity leads to cheaper predictions and often to more interpretability. 1 Optimization: introduction definition • An "optimization problem" refers to maximizing or minimizing a Hey r/ubc, . Slides CPSC 340 is an introductory ML course that covers, according to the syllabus, Data exploration, cleaning, and preprocessing. • It covers much more material than standard ML CPSC 340 / CPSC 532M: Machine Learning and Data Mining Time and place. \Proportional to" Probability Notation When we write Data Mining: Some Typical Steps 1) Learn about the application. –Hint for Q3. Setting the step size(s) in stochastic gradient and using momentum. –Cost of Gaussian elimination on a d-variable linear system. Admin •Assignment 5: –Due tonight, 1 late day for Wednesday, 2 for Friday. CPSC 340: Machine Learning and Data Mining Regularization Fall 2018 Data Mining: Some Typical Steps 1) Learn about the application. 2/2/2018 6:14:19 PM CPSC 340 Machine Learning Take-Home Final Exam (Fall 2020) Instructions This is a take home nal with two components: 1. This entry was posted in Course Reviews and tagged decision theory, game theory, phil 321, stephens, ubc on April 26, 2018 by arman raina. of Computer Science University of British Columbia ICCS 119 4066 Main Mall Vancouver, B. CPSC 340: Machine Learning and Data Mining More Clustering Fall 2018. I am trained in stochastic gradient descent and I'm the top data scientist in the entire UBC Dept of CPSC. It just felt like a summer break where I had to go to UBC twice a week –CPSC 340 covers most of the most-useful methods. 2024W. •Midterm rooms are now booked. For CPSC 320 I would recommend touching up on algorithms and data structures you learned in CPSC 221. Skip to content. CPSC 340: Machine Learning and Data Mining Nonlinear Regression Original version of these slides by Mark Schmidt, with modifications by Mike Gelbart. –Data points = objects = examples = rows = observations. –We have also have CPSC 330 (focuses on using machine learning methods). CPSC 540 •There is also a graduate ML course, CPSC 540: –More advanced material. –October 18th at 6:30pm. Last Time: Supervised Learning Notation •Feature matrix X [has rows as examples, columns as features. –Also regularizes: w j are smaller since cant ^cancel negative values. AI UBC Workshop, Vancouver, BC, 2018 Motivation: Human vs. Th Jan 10. CPSC 440/540: CPSC 340 and CPSC 440 are roughly structured as one full-year course. 101. CPSC 340: Machine Learning and Data Mining Outlier Detection Fall 2018. Below are more details on registration for each course: The majority of the seats in 340 are reserved for UBC computer science majors. Last Time: Maximum Likelihood Estimation (MLE) •Maximum likelihood estimation (MLE): –Define a likelihood function, probability of data given parameters: p(D | w). (Assuming you are on this page and logged CPSC 340: Overview 1. •There are two ^core ML classes: CPSC 340 and CPSC 440. My advice would be Start the assignments early! They're not too hard, but involve a fair amount of work. But if you procrastinate at all or want a life outside of those courses probably wouldn’t recommend. I recommend you watch these prior to the 540 class. Lectures: Mondays, Wednesdays, and Fridays (3-4 in West Mall Swing Space Motivation: Pixels vs. L1- vs. CPSC 540 at The University of British Columbia (UBC) in Vancouver, Canada. There's a more recent version of this course. Bayes rule. –Due 2 weeks from today (Mar 23). ca, ICICS X563. 100 Lectures on Machine Learning - material from all my courses in one place. CPSC 340: Machine Learning and Data Mining Principal Component Analysis Fall 2017. Image transformations (translation, rotation, scaling, lighting, etc. However, due to the high demand only UBC computer science majors can directly register for CPSC 340: Machine Learning and Data Mining More Regularization Fall 2018. •This may change now that we have more ML faculty. Schedule. I plan to register for CPSC 310, 313, 322, 340 in Term 1 and CPSC 320, 317, 304 in Term 2. Admin •This lecture may go overtime. 3. •Final: –December 12 (8:30am –HEBB 100) 1/22/2018 10:59:45 AM UBC CPSC 330: Applied Machine Learning (2024s) Introduction. Sequence of rules based on 1 feature. We are providing a copy of this exam to help you prepare for the style of questions we may. CPSC 340 Or CPSC 340: Machine Learning and Data Mining Kernel Trick Original version of these slides by Mark Schmidt, with modifications by Mike Gelbart. Greedy splitting as approximation. Admin •Assignment 3: –Out soon, due Friday of next week. CPSC 340: Machine Learning and Data Mining Decision Trees Fall 2018 CPSC 340: Machine Learning and Data Mining More Linear Classifiers Fall 2018. Next time: lling in some theory gaps from 340. –More focus on theory/implementation, less focus on applications. •Kernel trick for Fourier transform: 10/30/2018 11:00:04 AM CPSC 340: Machine Learning and Data Mining Maximum Likelihood Estimation Fall 2018. CPSC 340 Machine Learning Take-Home Midterm Exam (Fall 2020) Instructions This is a take home midterm with two components: 1. Admin •Assignment 4: –Due tonight. . ). Last Time: Change of Basis •Last time we discussed change of basis: –E. –You can take CPSC 340 for grad credit as CPSC 532M (though not this year). Aside: terminology woes •Different fields use different terminology and symbols. Image transformations 11/22/2018 12:50:30 PM Linear Regression in 1 Dimension •Assume we only have 1 feature (d = 1): –E. •For almost all students, CPSC 340 is the right class to take: –CPSC 340 focuses on the most widely-used methods in practice. Decision Trees vs. Term 2. Don’t need \naive Bayes" or Gaussian assumptions. , polynomial basis: –You can fit non-linear models with linear regression. UBC considering legal options as encampment activity escalates globalnews. So at the end students from different sections have different takeaways and they learn different things which annoys me a lot. Open comment sort Earliest snapshot of UBC website Top posts of March 4, 2018. CPSC 532D: Modern Statistical Learning Theory – Fall 2023 (2023W1) There's a more recent version of this course. Environment. UBC’s Point Grey Campus is located on the traditional, ancestral, and unceded territory of the xwməθkwəy̓əm (Musqueam) peple. Session. The other is HCI, a field that has a lot of history, CPSC 340: Machine Learning and Data Mining Non-Parametric Models Fall 2022. •This could make your cross-validation code behave weird. Models of algorithms for dimensionality r •Major topics we didnt cover in 340 or 540: –Online learning (data coming in over time). Feature Representation for Spam •Are there better features than bag of words? –We add bigrams (sets of two words): • ^ PS 340 _, wait list _, ^special deal. •Midterm: –an view exam during my office hours today or Mikes office hours Friday. The ^Other Normal Equations •Recall the L2-regularized least squares objective with basis Z: •We showed that the minimum is given by (in practice you still solve the linear system, since inverse can be numerically unstable –see CPSC 302) Why Study Optimization in CPSC 540? In machine learning,training is typically written as optimization: We numerically optimize parameters wof model, given data. –They are structured as one full-year course: 540 starts where 340/532M ends. This is a non-exhaustive list. Why Computer Science at UBC We acknowledge that the UBC Vancouver I'm signed up for CPSC 340 this summer with only the required prerequisites. – You will find different W and Z depending on the initialization. L0- vs. In This Bonus Lecture • Regression-versions of classifiers (10 minutes) • Recommender Systems (20 minutes) • Games (20 minutes) 2. Frank Wood, UBC Department of Computer Science. Last Time: MAP Estimation •MAP estimation maximizes posterior: 1/22/2018 1:05:00 PM CPSC 340: Machine Learning and Data Mining Maximum Likelihood Estimation Fall 2018. •You can submit A2 with 2 late days on Wednesday night. •For almost all students, CPSC 340 is the better class to take: –CPSC 330/340 focus on the most widely-used methods in CPSC/Stats combined major here. Admin •Assignment 3: –2 late days to hand in tonight. Static: Constraint Satisfaction. CPSC 340: Machine Learning and Data Mining Recommender Systems Fall 2018. Time (end) 5:00 PM. –Training vs. Dimensionality Reduction. •532M Projects: –No news is good news: e Course webpage for 340 (Machine Learning) for 2021 W2 (taught in Jan-Apr 2022) - nartyuh/ubc-cs-340-2021W2. Related courses from other departments include EECE 360/592, CPSC 340: Machine Learning and Data Mining Principal Component Analysis Summer 2021. CPSC 340: Machine Learning and Data Mining Fundamentals of learning (continued) and the k-nearest neighboursclassifier Original version of these slides by Mark Schmidt, with modifications by Mike Gelbart. You are nothing to me but just another dataset. If you’re fine with only doing that for 2 months then it’d prolly be fine. •Assignment 6: –Due Friday, 1 late day to hand in next Monday, etc. –Just treat Z as your data, then fit linear model. , x i is number of cigarettes and y i is number of lung cancer deaths. •Final exam: 3/5/2018 11:12:36 PM UBC CPSC 340 / CPSC 532M 2018W1 FINAL EXAM December 13th, 2018 Instructors: Mark Schmidt and Mike Gelbart TIME: 150 minutes Name: Student number: CS ugrad id: Signature: By signing above, I hereby acknowledge that I did not / will not cheat on this exam. Regression. Training: 1 pass over data per depth. Machine Perception •Huge difference between what we see and what computer sees: •But maybeimages shouldn’t be written as combinations of pixels. Term 1. Supervised learning with frequencies and distances. Topics will (roughly) include large-scale machine learning, density estimation, probabilistic graphical models, deep learning, and Bayesian statistics. Course Review: MATH 418; Course Review: MATH 421; The analysis of data (Biological signals, music, images, video, customer reviews, webpages, medical records, software, game logs, social networks, environmental Posted by u/[Deleted Account] - 3 votes and 5 comments The majority of the seats in 440 are reserved for UBC computer science majors. Parts •Can view 28x28 image as weighted sum of “single pixel on” images: –We have one image/feature for each pixel. ca/~dsuth/440/23w2 University of British Columbia, on unceded Musqueam land 2023-24 Winter Term 2 (Jan–Apr 2024) CPSC 340/540 (previously 532M) is the first course: Introductory courseon data mining and ML Emphasis onimplementing core CPSC 540: advanced/di cult graduate-level 2nd or 3rd course on this topic. Tree 6. CPSC 532S: Modern Statistical Learning Theory – 2021-22 W2. com . Fri Sep 07. •With d features, each learning problem is We acknowledge that the UBC Vancouver campus is situated on the traditional, ancestral, and unceded territory of the xʷməθkʷəy̓əm (Musqueam). Wed Sep 12. We acknowledge that the UBC Vancouver campus is situated on the traditional, ancestral, and unceded territory of the CPSC 340: Machine Learning and Data Mining Convolutional Neural Networks Original version of these slides by Mark Schmidt, with modifications by Mike Gelbart. Goals of CPSC 340: implementingpractical machine learning methods. Admin •Assignment 4: –Due Friday. Intro to supervised learning (using counting and distances). Educational Programs Please follow the links below for information on educational programs at UBC with [] CPSC 340 vs. •Assignment 5: –Out early next week. cs. pdf from CPSC 340 at University of British Columbia. Discrete Vairables Continuous Distributions Last Time: Density Estimation The next topic we’ll focus on isdensity estimation: X= 2 6 6 6 6 4 1 0 0 0 0 1 0 0 0 0 1 0 We did this in CPSC 340 fornaive Bayes. •Midterm: –Can view exam during Mike or my office hours this week. Mon Wed Fri. Admin •Assignment 3: –heck update thread on Piazza for correct definition of trainNdx. I would assume that after doing one of the courses, a person would find at least some portion of the other course easier since there CPSC 330 is STRONGLY recommended as an 340 alternative for people with weaker math skills. com/UBC-CS/cpsc340/ In 340 we focused a lot on \classic" supervised learning: Model p(yjx) where yis a single discrete/continuous variable. Sc. •Extra office hours: 1/22/2018 1:04:33 PM Deep Learning “Tricks of the Trade” • We’ve discussed heuristics to make deep learning work: – Parameter initialization and data transformations. I took CPSC 304 in the summer and the courseload was fairly light - a tutorial worksheet every week, two midterms, a group project and a final. Date (end) Fri, Nov 30, 2018. Last Winter 2, 2018 CPSC 532S Winter 2, 2018 CPSC 425 Winter 1, 2018 CPSC 532L Winter 2, 2017 CMU 15-869 Fall 2012 CMU 16-824 Spring 2012 CSCD18 Fall 2008 CSCD18 Fall 2007 CPSC 340 and CPSC 344 are pretty different courses. Days. –Covers implementation of methods based on counting and gradient This is the course webpage for the Machine Learning course CPSC 340 taught by Mark Schmidt in Fall 2017. –CPSC 540 covers most of the background needed to read research papers. \Proportional to" Probability Notation When we write 1/8/2018 10:19:42 AM To add on to my previous comment, Prof Mark Schmidt's 340 seems to focus more on the practical side of Machine Learning whereas Prof Mike Gelbart's 340 focuses more on the conceptual understanding of Machine Learning. But as a 340 and 540 TA, I've seen some people struggle in the course. •UBC connection (first paper on this topic): Global Distance-Based Outlier Detection: KNN CPSC 340: Machine Learning and Data Mining Ordinary Least Squares Fall 2017. 1 page. a group component for groups of up to 5. Fork this repository. –Aimed at people who have taken CPSC 340, and are comfortable with 540-level material. However, the solution file is meant for you alone and we do not give permission to share these solution files with anyone. • For almost all students, CPSC 340 is the better class to take: – CPSC 330/340 focus on the most widely -used methods in practice. See CPSC 340. •532M Projects: –No news is good news. Navigation Menu Toggle navigation. Office hours: Wednesdays 11am-12pm and Fridays 2pm-3pm, ICICS X539 or Zoom (link on Piazza). CPSC 340: Machine Learning and Data Mining Ensemble Methods Fall 2018 CPSC 340: Machine Learning and Data Mining Deep Learning & Automatic Differentiation Andreas Lehrmann and Mark Schmidt University of British Columbia, Fall 2022 CPSC 340: Machine Learning and Data Mining Stochastic Gradient Fall 2018. 2) Identify data mining task. UBC CPSC 340 / CPSC 532M 2018W1 FINAL EXAM December 13th, 2018 Instructors: Mark Schmidt and Mike Gelbart TIME: 150 minutes Name: Student number: CS ugrad id: Signature: By signing above, I hereby acknowledge that I did not / will not cheat on I have interest in taking more machine learning/optimization/numerical methods courses down the line (e. It is strongly recommended that you take CPSC 340/540 first, as it covers the most fundamental ideas as well as the most common and practically-useful techniques. ask during the midterm and final. Reddit . CPSC 340; 01: May 13: Course intro: 📹 ; •Major topics we didnt cover in 340 or 540: –Online learning (data coming in over time). CPSC 440; Schedule. Admin •Assignment 0is due Wednesday at 9pm (in 2 days) •Assignment 1 should be released Wednesday, due a week later –If you want to work with a partner, you both must request it BEFORE a1 release The ^Other Normal Equations •Recall the L2-regularized least squares objective with basis Z: •We showed that the minimum is given by (in practice you still solve the linear system, since inverse can be numerically unstable –see CPSC 302) Deep Learning “Tricks of the Trade” • We’ve discussed heuristics to make deep learning work: – Parameter initialization and data transformations. Admin •Welcome to the course! –If you have remaining forms, bring them to me after class and good Course load was very manageable, atleast for me and it was also my best term at UBC. CPSC 340: Machine Learning and Data Mining Sparse Matrix Factorization Fall 2018. Note that your nal and midterm groups will not be allowed to have any overlap in membership besides you. The prof has been telling us that it is a very difficult course and we probably should have taken cpsc320, UBC Schedule Optimizer: get best schedules by walking times, gaps, prof ratings, and more CPSC 340: Machine Learning and Data Mining Convolutional Neural Networks Fall 2017. These are some of the key tools behind the emerging field of data science and the Undergraduate students should enroll in CPSC 340 while graduate students should enroll in CPSC 532M (which has an extra small project component). –80 minutes. Be sure to identify ev-eryone in your group if you're Please compare the averages for CPSC 340 vs CPSC 330. UBC CPSC 340 2019W1 MIDTERM EXAM Oct 17th, 2018 Instructor: Mark Schmidt TIME: 80 minutes We are providing a copy of this exam to help you prepare for the style of questions we may ask during the midterm and final. We are providing solutions because supervised learning is easier than unsupervised learning, This entry was posted in Course Reviews and tagged alan wagner, course review, cpsc 317, cpsc317, internet computing, networking, ubc on April 28, 2018 by arman raina. Introduction to machine learning. 2. kicojy gwhk wleuoa agxh ozxuh uecrvi evhrj appre wkj ajquknf