Cmu 10315 pdf. Recommender Systems 10 ey.
Cmu 10315 pdf Formulate the likelihood times the prior, 𝒟𝜃 (𝜃) 2. XT X= 25 7 Naïve Bayes - CMU School of Computer Science Reminder: Machine Learning Problem Formulation Three components <T,P,E>: 1. As the distance between x and z increases, the output falls off towards zero. Table 2: Feature-Engineered Dataset D′ Now, we can train a linear regression model with this new dataset D′ 10-315 Notes Neural Networks Carnegie Mellon University Machine Learning Department Contents 1 Neural Networks1 1. Don’t forget to submit the associated Programming component on Gradescope if there is any programming required. Dimensionality Reduction: The transformation of data from a high-dimensional space into a low- CMU School of Computer Science. Student A draws a new alien and 10-315 Machine Learning Exam 2 Practice Problems - Page 4 of 33 2 Logistic Regression 1. g. The first two are discrete distributions and the Gaussian distributions are continuous. If they are correctly there, it provides the optimal classifier (minimal expected loss) I’m taking 10315 rn with Virtue, and the lectures are so bad… is there a nice textbook or any way else I can learn the material? 10-315: Introduction to Machine Learning Recitation 5 Now we will calculate the derivative with respect to W 1,1,2: ∂J ∂W 1,1,2, where W 1,1,2 is the 1,2 entry in W 1 weight matrix. Bias depends on the model you use (in this case linear regression) and not Any of these courses must be satisfied to take the course: 10301 or 10315 or 10715 or 10601 or 10701. Participation In-Class Polls 5 Q2. , because the event must belong to one of the Kclasses). Identifytherelationshipsbetweentheconceptsofloss The Multivariate Gaussian Distribution Chuong B. Yours might already be answered! geometry and nearest neighbors 31 After this mapping, you can think of a single example as a vec- tor in a high-dimensional feature space. Please be aware that extensions are entirely discretionary and will be granted only in exceptional circumstances outside of your control (e. (3 points) Draw a dataset of a total of 4 unique points with input in R2, where 2 of the points have positive labels (drawn as ×) and 2 of the points have negative labels (drawn We use Gradescope to collect PDF submissions of open-ended questions on the homework (e. As a warmup, the written component of the assignment will lead you through an on-paper example of how to implement a neural network. pdf Exercise: Human-defined Feature Space Try to organize data on a 2-D coordinate plot to win the following game: Select three students: A, B, C 1. OptimizationforML a. CMU School of Computer Science Recipe for Estimation MAP 1. (6 points) Assume we have data D= {x(i)}N i=1 and assume our x (i) are i. pdf Mitchell_Ch (Secs 1-2) September 11 Wednesday Logistic Regression Lecture5. Implement learning for linear regression using four optimization techniques: (1) closed form, (2) (batch) gradient descent, (3) stochastic gradient descent, and 10-315: Introduction to Machine Learning Recitation 9 2 SVD (a)Find the SVD of X= 4 4 3 3 To nd the SVD of X, we rst compute the matrices X TXand XX . If a point, x is close to z, then the output reaches its maximum value of one. You need to have, before starting this course, college-level maturity in discrete mathematics, as can be achieved at CMU by having passed 21-127 (Concepts of Mathematics) or 15-151 (Mathematical Foundations of Computer Science), or comparable k-Nearest Neighbor Classification Given a training dataset 𝒟=𝑦 ,𝒙 =1 𝑁,𝑦∈1,…,𝐶,𝒙∈ℝ and a test input 𝒙 𝑒 , predict the class label, 𝑦ො 𝑒 5 Q2. Also for 10301/10315, make sure you know your probability well, since the second half of the class does have a lot of probability. d from a distribution with the following density function: f(x;λ) = λx 2 PCA Math Background 2. 1 Projections 2. I've heard 10315 is actually easier than 10301, but 10301 itself isn't too bad. Recommender Systems 9 ey. i. Task, T 2. Recommender Systems 11 Problem Setup • 500,000 users • 20,000 movies 10-315: Introduction to Machine Learning Recitation 2 Outlook (X 1) Temperature (X 2) Humidity (X 3) Go on run?(Y) sunny hot high no sunny mild high no sunny mild normal yes From now on, we will be strictly considering the branch of the decision tree where the outlook is sunny. , due to severe illness or major personal 1 Distributions We’ll focus on just a few key distributions: Bernoulli, categorical, Gaussian, and multivariate Gaus-sian. Instead, we will include the correct answer in the Gradescope rubric. [3 pts] Suppose we have a two-dimensional input space such that the input vector is x = [x • How to submit written component: Submit to Gradescope a pdf with your answers. 2 Linear systems of equations underdetermined: If there are fewer equations than variables, the system is underdetermined and CMU School of Computer Science 10-601 Machine Learning Maria-Florina Balcan Spring 2015 Generalization Abilities: Sample Complexity Results. CMU School of Computer Science Vector notation: 1. 4; 11/14 : Gaussian Mixture Models, Clustering, and EM: Derivation of general form of EM algorithm; EM for GMMs; general properties and limitations of EM; study of special cases for GMMs, relations to clustering (K-means). Let binary Y be a random variable representing a coin flip. The Eberly Center for Teaching Excellence and Educational Innovation is located on the CMU-Pittsburgh Campus and its mission is to support the professional development of all CMU instructors regarding teaching and learning. The most typical student is an MS student from SCS; but the course is intended to allow students from anywhere in the university, including those whose mathematical backgrounds may be rusty or incomplete, to catch up and do well. The probability of each Y kbeing one is denoted by ϕ k, and all of the ϕ k’s sum to 1 (e. mathematical derivations, plots, short answers). pdf, Lecture2_inked. Log in and click on our class 10-315, click on the appropriate Written assignment, and upload your pdf containing your answers. Always fits a linear (or affine) shape to the data B. [2 pts] What shape is function J(w 1,w 2;D) when you plug in the values for a,b,c,d,e,fthat you CMU School of Computer Science Homework 7 10-315 Introduction to Machine Learning (Spring 2024) 2[26 pts] Priors and Regularization In this problem, we are going to have you work through the proof that adding a Laplace prior to proba- There will be four homework assignments that will have some combination of written and programming components and five online assignments. pdf Mitchell_Ch QnA 1 due, HW1 out (Naive Bayes & LR) September 9 Monday Naive Bayes, MAP, Continuous features Lecture4. Hint: Start by expanding Av and then expanding vTAv. pdf Mitchell_Ch (Secs 3-5 Softmax Function Softmax function convert each value in a vector of values from −∞,∞→(0,1), such that they all sum to one. Then, you will implement an end-to-end system that learns to perform How to submit: Submit a pdf with your answers on Gradescope. 10-315: Introduction to Machine Learning Recitation 4 4 K=2: Multi-class vs. 1 Vocab/definitions: linear combination: If x 1,,x K are vectors in RM, and c 1,,c K are scalars ∈R, then the resulting vector, c CMU School of Computer Science Support Vector Machines - CMU School of Computer Science CMU School of Computer Science Model selection Best practices Aarti Singh Machine Learning 10-315 Nov 8, 2021 Poll 1: Exercise Implement a function in Python for the pdf of a Gaussian distribution. The Eberly Center may provide support on this research project regarding data analysis and interpretation. 10-315: Introduction to Machine Learning Recitation 7 1 Definitions Ahoy! 1. Format: Complete this pdf with your work and answers. Examples of Model Spaces Model Spaces with increasing complexity: •Nearest-Neighbor classifiers with increasing neighborhood sizes k = 1,2,3,… Small neighborhood => Higher complexity 10-315 Machine Learning Exam 2 Practice Problems - Page 4 of 33 2 Logistic Regression 1. Implementlearningforlinearregressionusingfouroptimizationtechniques:(1) closedform,(2)(batch)gradientdescent,(3)stochasticgradientdescent,and Generative Models: Supervised vs Unsupervised Discriminant analysis vs Gaussian mixture models =argmax 𝜃 ෑ 𝑖 𝑘=1 𝐾 𝑖, 𝑘 (𝑖)=1∣𝜃 =argmax 4 The logical path that we are following oFacts: Following a generative approach, the Bayes optimal classification rule requires probabilistic models to be in place. org NEC Labs America, Princeton, NJ 08540, USA Chih-Jen Lin cjlin@csie. 12 a course in machine learning about this is to look at the histogram of labels for each feature. [0 pts] Decision Trees Perceptron Trees: To exploit the desirable properties of decision tree classifiers and perceptrons, Adam came up with a new algorithm called “perceptron trees”, which combines features from both. Binary Logistic Regression In the special case where K= 2, one can show that multi-class logistic regression reduces to binary logistic Slide credit: CMU MLD Matt Gormley 8. Regularized Linear Regression Aarti Singh Machine Learning 10-315 Sept 22, 2021 CMU School of Computer Science MLG 10315 at Carnegie Mellon University (CMU) in Pittsburgh, Pennsylvania. The ability to generalize beyond what we have seen in the training phase is the essence of machine Loss Functions ℎ∗=argmin ℎ 𝔼 𝐿 ,ℎ Loss function: 𝐿: × →ℝ Classification: Two-class, 0,1 loss Two-class, arbitrary loss Regression: Non-parametric methods Aarti Singh Machine Learning 10-315 Oct12, 2020 CMU School of Computer Science This studio will investigate the role and process of architectural design as different forms of practice. plw. MLE: Finds the best parameters for a specific dataset,D. 3 Bayes rule: ML input and output data What if we take the generic Bayes rule formula and use variables xand y, where xand yrepresent the input and output of a machine learning prediction problem: Support Vector Machines (SVMs) Aarti Singh Machine Learning 10-315 Oct 20, 2021 Homework 3 10-315 Introduction to Machine Learning (Spring 2024) Work (optional) 2. cmu. LinearRegression a. Announcements Assignments HW10 (programming + “written”) Due Thu 4/30, 11:59 pm Final Exam Stay tuned to Piazza for details Date: Mon 5/11, 5:30 –8:30 pm 3. Describetrade-offsbetweendifferentoptionsforcross-validation NumericalOptimizationforML 1. y x x2 5 6 36 12 7 49 1 3 9 5 2 4 15 0 0. We get these kinds of questions a lot, and having the answers in one place is more helpful for everyone. Dec 5, 2024 · Carnegie Mellon University. Assuming denominator layout, prove δ δv v TAv = (AT+ A)v for v ∈R2 and A∈R 2×. = 1 Announcements Assignments HW6 (written + programming) Due Thu 3/26, 11:59 pm “Participation” Points Polls open until 10 am (EDT) day after lecture Learning Theory Aarti Singh Machine Learning 10-315 Nov 22, 2021 Slides courtesy: Carlos Guestrin 10-315: Introduction to Machine Learning Recitation 11 4 Hierarchical Clustering Example Consider the five data points shown below (placed at the center of the diamond representation). We use Gradescope to collect PDF Welcome to r/cmu! Please use the megathread instead of making a new post for questions about admissions, transfers, and general CMU info like majors and dorms. , ∥v∥ This lecture covers the fundamental concepts of computer science taught at CMU. • How to submit programming component: See section Programming Submission for details on how to submit to the Gradescope autograder. Question 1: Derive the likelihood function p(D|θ) and compute the maximum likelihood estimates for µ and σ2 using the log-likelihood function. pdf: Bishop Chapters 9. 4 Instructor: Barnabas Poczos, Machine Learning Department Education Associate: Daniel Bird, (dpbird [at] andrew [dot] cmu [dot] edu) Machine Learning Department When/where: POS 153, 10:10-11:30pm, Mondays and Wednesdays Recitations: POS 153, 10:10-11:30pm Fridays Class website: pptx pdf MLE notes (draft; last section added soon!): pdf: MML 9 Bishop 1. If your PDF is misaligned, you will receive a 2% penalty on that assignment. The course staff will manually grade your submission, and you’ll receive personalized feedback explaining your final marks. tw Department of Computer Science 10-315 Intro to ML Midterm Exam 2 - Page 8 of 22 3 MLE and MAP 1. Following CMU policy, students that have symptoms of COVID-19 should contact University Health Services (UHS) at 412-268-2157. 1 Scalar Projection Given two vectors in RN denoted x and v, the scalar projection of x onto v is defined as: d= v⊤x ∥v∥ 2 Note that if we assume that v is a unit vector, i. Recommender Systems 10 ey. e. How to submit: Submit a pdf with your answers on Gradescope. k(x,z) = e−γ∥x−z∥22 Carnegie Mellon University Summary In this assignment, you will build a handwriting recognition system using a neural network. 1 Networks Diagrams for Linear and Logistic Plan Today Wrap-up regularization (for now) MLE Probability / likelihood Maximum likelihood estimation Probabilistic formulation of linear and logistic regression 27 K- means clustering Partitioning Algorithms • K-means –hard assignment: each object belongs to only one cluster • Mixture modeling –soft assignment: probability that an object belongs to a cluster Decision Trees Example Problem Consider the following data, where the Y label is whether or not the child goes out to play. Linear Regression a. If you have D-many fea-tures (after expanding categorical features), then this feature vector September 4 Wednesday Decision Boundary, Naive Bayes, MLE Lecture3. This document from CMU School of Computer Science discusses the Bayes classifier and decision boundaries. Experience, E Definition of learning: A computer program learns if its performance at tasks in T, as Machine Learning Tasks 39 Broad categories - •Supervised learning Classification, Regression •Unsupervised learning Density estimation, Clustering, Dimensionality reduction 10-315: Introduction to Machine Learning Recitation 4 1 Notation and Definitions 1. 4-5, 3. 2. From the official course selection guide posted on the ML course website: 10-301/10-601: students in this course have the most diverse collection of backgrounds. Decision Stumps Split data based on a single attribute Majority vote at leaves Dataset: Output Y, Attributes A, B, C Y A B C-1 0 0 10-315: Introduction to Machine Learning Recitation 9 2 SVD (a)Find the SVD of X= 4 4 3 3 To nd the SVD of X, we rst compute the matrices X TXand XX . 3. Recommender Systems 11 Problem Setup • 500,000 users • 20,000 movies 10-315: Introduction to Machine Learning Recitation 5 Now we will calculate the derivative with respect to W 1,1,2: ∂J ∂W 1,1,2, where W 1,1,2 is the 1,2 entry in W 1 weight matrix. (3 points) Draw a dataset of a total of 4 unique points with input in R2, where 2 of the points have positive labels (drawn as ×) and 2 of the points have negative labels (drawn Poll 2 Linear regression. 2 PRACTICE QUESTION ANSWERS • If you train a linear regression estimator with only half the data, its bias is smaller. How fast it falls of depends on the hyper parameterγ. XT X= 25 7 3. 1 MLE/MAP 1. SOLUTION: FALSE. edu, as soon as possible and before the deadline. 10-315 Intro to ML Midterm Exam 1 - Page 3 of 14 2. Machine learning is a subfield of computer science with the goal of exploring, studying, and developing learning systems, methods, and algorithms that can improve their performance with learning from data. 1. Experience, E Definition of learning: A computer program learns if its performance at tasks in T, as 10-315 Intro to ML Midterm Exam 1 - Page 4 of 16 2. edu. Linear objective function with respect to Worksheet: pdf kNN. pdf 10/23 Neural networks 5: Autoencoders, Transformers, GANs - concepts and implementation of autoencoders for dimensionality reduction, compression, denoising; general ideas about transfer learning and generative networks; examples of transformer, chatGPT; Generative Adversarial Networs. • How to submit: Submit a pdf with your answers on Gradescope. Course Info. 1-2 2/27 Mon : MLE and Probabilistic Modeling : See previous lecture slides 3/1 Wed : EXAM 1 In-class : Learning objectives: pdf Practice problems: pdf 3/6 Mon : No class: Spring Break: 3/8 Wed I’m taking 10315 rn with Virtue, and the lectures are so bad… is there a nice textbook or any way else I can learn the material? Carnegie Mellon University Teaching team: Lecture: Aarti Singh, Instructor Mary Stech, Class Assistant Fabricio Flores, Education Associate Siddharth Ancha, TA TBA, TA Day and Time: Monday and Wednesday, 10:30 - 11:50 am Location: HOA 160 Recitation: Day and Time: Friday, 10:30-11:50 am Location: HOA 160 Machine Learning is concerned with computer programs that automatically improve their performance through experience (e. ipynb: 2/2 Fri : Recitation 3: Matrix Calculus and Linear Regression : pdf 2/9 Fri : Recitation 4: Logistic Regression : pdf 2/16 Fri : Recitation 5: Neural Networks : pdf 2/22 Fri : Recitation 6: Regularization, Prob/Stat/MLE : pdf 3/1 Fri Radial basis function (RBF): This filter isvery similar to a Gaussian pdf. [2 pts] If today I want to predict the probability that a student sleep more than 8 hours Dec 5, 2024 · You are required to check that the PDF you upload to Gradescope matches the provided template. In SVM the goal is to nd some hyperplane which separates the positive from the negative examples, such Announcements Assignments HW7 (written + programming) Due Tue 3/31, 11:59 pm HW8 (written + programming) Out this week Due Tue 4/7, 11:59 pm Support Vector Machines (SVMs) contd… Aarti Singh Machine Learning 10-315 Oct 27, 2021 d. pdf: Murphy: Sec 1. Performance measure, P 3. 5 7 Day Weather Temperature Humidity Wind Play? 5 Q2. Machine Learning is concerned with computer programs that automatically improve their performance through experience (e. Whether you edit the latex source, use a pdf annotator, or hand write / scan, make sure that your answers (tex’ed, typed, or handwritten) are within the dedicated regions for each question/part. pdf Slides on GMMs (Barnabas Poczos, 10-401) Bishop Chapters 9. 2 Linear(andaffine)functionsandgeometry 3. CS229Lecturenotes Andrew Ng Part XI Principal components analysis In our discussion of factor analysis, we gave a way to model data x ∈Rd as “approximately” lying in some k-dimension subspace, where k ≪d. 1Kernel Computation Cost 1. As a rule, we never release PDF solutions for any homework. (10301 or 10315 or 10601 or 10701 or 10715 or 11485 or 11685 or 11785). Written and online components will involve working through algorithms presented in the class, deriving and proving mathematical results, and critically analyzing How to submit: Submit a pdf with your answers on Gradescope. 3: September 2 Wednesday: Intro to ML concepts: Intro_contd. support vector machines (vhqg fruuhfwlrqv wr \nv dw fvdlo. 10-315: Introduction to Machine Learning Recitation 11 1 Definitions to Go 1. Syllabus Course Info. Announcements Assignments HW7 (written + programming) Due Tue 3/31, 11:59 pm HW8 (written + programming) Out this week Due Tue 4/7, 11:59 pm 10-315 Notation Guide Based on 10-301/601 Notation Guide by Matt Gormley 1 Scalars, Vectors, Matrices Scalars are either lowercase letters x;y;z; ; ; Announcements Assignments HW6 (written + programming) Due Thu 3/26, 11:59 pm HW7 (online) Out later tonight Due Tue 3/31, 11:59 pm class kand 0 otherwise. Homework 10 10-315 Introduction to Machine Learning (Spring 2024) 3[9 pts] Kernels 3. 1 1 A colleague related the story of getting his 8-year old nephew to guess a number between 1 and 100. The probability of outcome heads, Y = 1 is modeled by a Bernoulli distribution with parameter ϕ ∈[0,1], i. 10-315: Introduction to Machine Learning Recitation 9 2 Support Vector Machines Assume we are given dataset D= f(x i;y i)gn i=1, where x i2R dand y i2f+1; 1g. 1 PCA 1. For example, if Ztakes on the value 1 when the roll of a six-sided fair dice is even, the probability of rolling an even number will be denoted as the following: P(Z= 1) = 1 2 Slide credit: CMU MLD Matt Gormley 8. Recall that the pdf for a univariate Gaussian is given by the following equation p(x) = 1 √ 2πσ2 e− (x−µ2) σ2. The studio will practice drawing, making, and building architectural narratives in iterations at various scales of time and space, to establish productive habits and develop essential techniques and skills in architectural design. 1 Matrix Vector Multiplication The application of a matrix A∈Rn× mto a vector v∈R is the matrix vector multiplication Av. What is linear about it? Select all that apply. 03/11 – present Adjunct Professor, Department of Chemistry, Carnegie Mellon University, Pittsburgh, PA 15213 07/10 – 06/14 Associate Professor, Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, PA 15213 Nonparametric density estimation •Histogram •Kernel density est Fix D, estimate number of points within Dof x (n ior n x) from data Fix n x= k, estimate Dfrom data (volume of ball 10-315: Introduction to Machine Learning Recitation 3 3 How About a Proof? 1. Machine Learning Systems Three components <T,P,E>: 1. If you do not follow this format, we may deduct points. Python numpy or math packages are fine, no scipy, etc. 1-1. Even if you know you do not have COVID-19 but have symptoms that may be a sign of other contagious illnesses, such as a cold or flu, please do not come to class. A. Compute the magnitude of the projections, i. CMU School of Computer Science How to submit: Submit a pdf with your answers on Gradescope. 2-9. ntu. Instructors: Henry Chai and Matt Gormley; Meetings: 10-301 + 10-601 Section A: MWF, 9:30 AM - 10:50 AM (DH 2315) Density Estimation • A Density Estimator learns a mapping from a set of attributes to a Probability Density Estimator Probability Input data for a variable or a set of a mapping of events to values, and then the associated pmf (or pdf) maps those values to probabilities (or densities). [2 pts] If today I want to predict the probability that a student sleep more than 8 hours Slide credit: CMU MLD Matt Gormley. Experience, E Definition of learning: A computer program learns if its performance at tasks in T, as Support VectorMachine Solvers SupportVectorMachineSolvers L´eon Bottou leon@bottou. 2. ) as an official Academic Integrity Violation, in compliance with CMU's Policy on Academic Integrity, and will carry severe penalties. ipynb DT. . 𝑔 V = 𝑒𝑧j σ =1 𝐾𝑒𝑧𝑘 CMU School of Computer Science Following CMU policy, students that have symptoms of COVID-19 should contact University Health Services (UHS) at 412-268-2157. 10-315: Introduction to Machine Learning Recitation 3 Figure 1: Procedure of Softmax Regression with 3-dimensional Features Softmax Regression For K-class classi cation, Softmax Regression has a parametric model of the form: • How to submit: Submit a pdf with your answers on Gradescope. Do October 10, 2008 A vector-valued random variable X = X1 ··· Xn T is said to have a multivariate normal (or Gaussian) distribution with mean µ ∈ Rn and covariance matrix Σ ∈ Sn Boosting Aarti Singh Machine Learning 10-315 Nov 3, 2021 Slides Courtesy: Carlos Guestrin, Freund & Schapire 1 Can we make dumb learners smart? Experimental Comparison (Ng-Jordan’01) 3 UCI Machine Learning Repository 15 datasets, 8 continuous features, 7 discrete features Naïve Bayes Logistic Regression 10-315: Introduction to Machine Learning Recitation 10 Suppose we want to project the centered data onto v, where v goes through the origin. , programs that learn to recognize human faces, recommend music and movies, and drive autonomous robots). If you think you really really need an extension on a particular assignment, e-mail Joshmin, joshminr@andrew. Again, make sure your answer boxes are aligned with the original pdf template. compute z(i) = vTx(i);81 i N. hgx) iq svmv zh duh wu\lqj wr ilqg d ghflvlrq erxqgdu\ wkdw pd[lpl]hv wkh "pdujlq" ru wkh "zlgwk ri wkh urdg" vhsdudwlqj wkh Slide: CMU ML, Tom Mitchel and Roni Rosenfeld ℎ T→ Uො 𝒟= T(𝑖), U(𝑖) 𝑖=1 𝑁 1 𝑁 𝑖=1 𝑁 𝕝 U𝑖≠ Uො𝑖 1 𝑁 𝑖=1 𝑁 U𝑖− Uො𝑖 2 Notation alert: Indicator function 𝕝 V=𝟏( V)=ቊ 1 if V is true 0 otherwise 10-315 Notation Guide Based on 10-301/601 Notation Guide by Matt Gormley 1 Scalars, Vectors, Matrices Scalars are either lowercase letters x;y;z; ; ; Instructions for Speci c Problem Types For \Select One" questions, please ll in the appropriate bubble completely: Select One: Who taught this course? Any violations of academic integrity will always be reported to the university authorities (your Department Head, Associate Dean, Dean of Student Affairs, etc. Support: Date Lecture Slides Useful links HWs; August 31 Monday: Intro to ML concepts: Lecture1. Set objective 𝐽(𝜃) equal to negative log of the likelihood times the prior 10-315 Intro to ML Midterm Exam 2 - Page 9 of 22 2. teky tus hfqenrow icaufv gvjic dsames rtszq yvgrx xlird pkxcyw