Announcements Reminder: pset5 out, due midnight today • pset5 self-grading form out Monday, due 11/16 (1 week) • pset 6 out next week 11/12 Reinforcement Learning Deep Mind’s bot playing Atari Breakout Machine Learning 2019, Kate Saenko 3 Reinforcement Learning • Plays Atari video games • Beats human champions at Poker and Go • Robot […]
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CS.542 Machine Learning, Fall 2020 I. Math and Probability Basics Q1: Review of Definitions (a) For a scalar random variable 𝑥, give the definition of its mean and variance. (b) For a vector random variable 𝑥 ∈ R𝑛, give the definition of its mean and covariance. Q2: Short questions (a) Given a vector 𝑣 ∈
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Announcements Reminder: Class challenge out! Ends December 10th • Final in two weeks, practice question will be posted today Class Challenge Task 2 • 100 labels isn’t enough! (see Lecture 21/first half of 22) • Transfer learning from a different dataset? if it isn’t a dataset we provided, you have to check with us (ImageNet
Announcements Reminder: pset5 self-grading form and pset6 out Thursday, due 11/19 (1 week) • No lab this week! Reinforcement Learning II Recall: MDP notation • S – set of States • A – set of Actions • 𝑅: 𝑆 →R (Reward) • Psa – transition probabilities (𝑝(𝑠, 𝑎, 𝑠′) ∈ R) • 𝛾 – discount
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Announcements Reminder: pset5 self-grading form and pset6 out, due Today 11/19 11:59pm Boston Time • Class challenge out Today (will discuss in class) Semi-Supervised Learning Slides credit: Jerry Zhu, Aarti Singh Supervised Learning Feature Space Label Space Goal: Optimal predictor (Bayes Rule) depends on unknown PXY, so instead learn a good prediction rule from training
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EECS3311 Software Design Fall 2020 Project Designing and Implementing the Space Defender 2 Game Chen-Wei Wang and Kevin Banh Released Date: Monday, November 2 Phase 1 Due Date (Sections A & E): 11:59pm (EST), Wednesday, November 25 Phase 2 Due Date (Sections A & E): 11:59pm (EST), Wednesday, December 9 Policies – Your (submitted or
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Announcements Reminder: ps2 due tonight at midnight (Boston) • Self-Grading form for ps1 out tomorrow 9/25 (1 week to turn in) • Self-Grading form for ps2 out Monday 9/28 (1 week to turn in) Unsupervised Learning II Agglomerative Clustering 2 slide credit: Andrew Ng cluster centroids slide credit: Andrew Ng slide credit: Andrew Ng slide
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Supervised Learning I: Regression Today • Multivariate linear regression • Solution for SSD cost – Indirect – Direct • Maximum likelihood cost Hypothesis: 500 400 300 200 100 0 0 1000 2000 3000 Linear Regression ‘s: Parameters Cost Function: SSD = sum of squared differences, also SSE = sum of squared errors Multidimensional inputs Size
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CS.542 Machine Learning, Fall 2019, Prof. Saenko Machine Learning Midterm Practice Problems Some of these sample problems had been used in past exams and are provided for practice, in addition to the homework problems which you should also review. A typical exam would have around 5 questions worth a total of 100 points. The exam
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CS.542 Machine Learning, Fall 2020 1. Math and Probability Basics Q1.1 Definitions [a] Give the definition of an orthogonal matrix. [b] Give the definition of an eigenvector and eigenvalue. [c] How is the probability density function different from the cumulative probability distribution? [d] What is a ‘singular’ matrix? [e] Give the definition of Baye’s Rule.
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