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CS 189COMPSCI 189
CS 189 at UC Berkeley. Introduction to Machine Learning. Lectures: T/Th 3:30-5 p.m., 155 Dwinelle. Professor Jennifer Listgarten jennl berkeley.edu. Office Hours: Tu/Th 5-6 p.m. (see calendar) EECS 189 INTRODUCTION TO MACHINE LEARNING NOTE 4 EECS 189 Introduction to Machine Learning Fall 2020 Note 4 1 MLE and MAP for Regression (Part I) So far, we’ve explored two approaches of the regression framework, Ordinary Least Squares and EECS 189 INTRODUCTION TO MACHINE LEARNING NOTE 11 EECS 189 Introduction to Machine Learning Fall 2020 Note 11 1 Canonical Correlation Analysis PCA provided us with a dimensionality-reduction approach that didn’t use the labels y inany way.
EECS 189 INTRODUCTION TO MACHINE LEARNING HW1 EECS 189 Introduction to Machine Learning Fall 2020 HW1 This homework is due Tuesday, September 8 at 11:59 p.m. 1 Getting Started Read through this page carefully.EECS 189
EECS 189
NOTE 2 - EECS 189
Note: Although we write (X>X) 1, in practice one would not actually compute the inverse; it is more numerically stable to solve the linear system of equations above (e.g. with Gaussian elimination). In this derivation we have used the condition rL(w ) = 0, which is a necessarybut not su cient
EECS 189 INTRODUCTION TO MACHINE LEARNING NOTE 10 1.1 Projection Let us first review the meaning of scalar projection of one vector onto another. If v 2Rd is a unit vector, i.e. kvk= 1, then the scalar projection of another vector x 2Rd onto v is given byx>v.This
NOTE 20 - EECS 189
Figure 1: Several possible decision boundaries under the perceptron. The X’s and C’s represent the +1’s and 1’s respectively. In the figure above, we consider three potential linear separators that CS 189CALENDARRESOURCESSYLLABUSABOUT Week 1 Overview. Foundations: Regression, Classification, and Learning & Features and Regularization. Monday, August 31 - Friday, September 4. Note 2 : Linear Regression. Note 3 : Feature Engineering. Discussion 1 ( solution) Homework 0 ( zip, datahub) Homework 1 CS 189 SYLLABUSCS 189CS 189 FALL 2019COMPSCI 189 If you are in 189 and do the project, we calculate your grade using both of the above schemes, and the higher of the two results will be used for your final grade. 289A students must do the project. CS 189/289A will not be curved in Fall 2020. The bins that will determine your grade are listed below: A: 90%.CS 189COMPSCI 189
CS 189 at UC Berkeley. Introduction to Machine Learning. Lectures: T/Th 3:30-5 p.m., 155 Dwinelle. Professor Jennifer Listgarten jennl berkeley.edu. Office Hours: Tu/Th 5-6 p.m. (see calendar) EECS 189 INTRODUCTION TO MACHINE LEARNING NOTE 4 EECS 189 Introduction to Machine Learning Fall 2020 Note 4 1 MLE and MAP for Regression (Part I) So far, we’ve explored two approaches of the regression framework, Ordinary Least Squares and EECS 189 INTRODUCTION TO MACHINE LEARNING NOTE 11 EECS 189 Introduction to Machine Learning Fall 2020 Note 11 1 Canonical Correlation Analysis PCA provided us with a dimensionality-reduction approach that didn’t use the labels y inany way.
EECS 189 INTRODUCTION TO MACHINE LEARNING HW1 EECS 189 Introduction to Machine Learning Fall 2020 HW1 This homework is due Tuesday, September 8 at 11:59 p.m. 1 Getting Started Read through this page carefully.EECS 189
EECS 189
NOTE 2 - EECS 189
Note: Although we write (X>X) 1, in practice one would not actually compute the inverse; it is more numerically stable to solve the linear system of equations above (e.g. with Gaussian elimination). In this derivation we have used the condition rL(w ) = 0, which is a necessarybut not su cient
EECS 189 INTRODUCTION TO MACHINE LEARNING NOTE 10 1.1 Projection Let us first review the meaning of scalar projection of one vector onto another. If v 2Rd is a unit vector, i.e. kvk= 1, then the scalar projection of another vector x 2Rd onto v is given byx>v.This
NOTE 20 - EECS 189
Figure 1: Several possible decision boundaries under the perceptron. The X’s and C’s represent the +1’s and 1’s respectively. In the figure above, we consider three potential linear separators thatCS189 RESOURCES
Tips. These tips have been collected through the years from professors, past and present. You can also check out the Learning How To Learn coursera for other general tips. Don't fall behind. In a conceptual class such as this, it is particularly important to maintain a steady effort throughout the semester, rather than hope to cram just before homework deadlines or exams. EECS 189 INTRODUCTION TO MACHINE LEARNING NOTE 3 EECS 189 Introduction to Machine Learning Fall 2020 Note 3 1 Feature Engineering We’ve seen that the least-squares optimization problem min w kXw yk2 2 represents the “best-fit” linear model, by projecting y onto the subspace spanned by the columns EECS 189 INTRODUCTION TO MACHINE LEARNING HW0 EECS 189 Introduction to Machine Learning Fall 2020 HW0 This homework is due Tuesday, September 1 at 11:59 p.m. Since no material from 189/289A is in scope for this homework, the problems in this homework were taken verbatim from the fresh-NOTE 20 - EECS 189
Figure 1: Several possible decision boundaries under the perceptron. The X’s and C’s represent the +1’s and 1’s respectively. In the figure above, we consider three potential linear separators thatDIS6 - EECS 189
EECS 189 Introduction to Machine Learning Fall 2020 DIS6 This discussion was released Friday, October 9. This discussion material is on PCA, LASSO, ridge regression and their relationships. EECS 189 INTRODUCTION TO MACHINE LEARNING NOTE 7 1.1.1 Optimization View From an optimization perspective, the problem can be expressed as wˆ wls = argmin w2Rd Xn i=1! i(y i 2x i >w) This objective is the same as OLS, except that each term in the sum is weighted by a positive CS 189 INTRODUCTION TO MACHINE LEARNING NOTE 25 2.Separate into a region x j < vwith 20 points of class 1 and 40 of class 2 (20=60 = 1=3 misclassified), and a region x j vwith 20 points of class 1 and 0 of class 2 (0=20 = 0 misclassified). Since 60=80 = 3=4 of the points lie satisfy x j See Syllabus for more information. * Homework 0: Getting Started (solution)
* Homework 1: Prerequisites (data)(solution
)
* Homework 2: Regression (data)(solution
)
* Homework 3: Probabilistic Regression(data) (solution
)
* Homework 4: Kernels, Correlations, and Total Least Squares(data)
(solution
)
* Homework 5: Optimization, Correlation, CCA(data)
(solution
)
* Homework 6: Backprop and Neural Networks(data)
(solution
)
* Homework 7: K-Means and EM (data)(solution
)
* Homework 8: SVMs and Decision Trees(data) (solution
)
* Homework 9: Ensemble Methods and Sparse Regression Revisited(data)
(solution
)
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