EECS 189 Introduction to Machine Learning Fall 2020 HW8 This homework is due Wednesday, October 28 at 11:59 p.m. 2 Towards understanding Few-shot Learning by Predicting Auxiliary La- bels Make sure to submit the code you write in this problem to “HW8 Code” on Gradescope. This problem helps us understand the “latent space” view of […]
EECS 189 Introduction to Machine Learning Fall 2020 This homework is due Wednesday, September 23 at 11:59 p.m. 2 Probabilistic Model of Linear Regression HW3 Both ordinary least squares and ridge regression have interpretations from a probabilistic stand- point. In particular, assuming a generative model for our data and a particular noise distribution, we will
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EECS 189 Introduction to Machine Learning Fall 2020 This homework is due Tuesday, November 17 at 11:59 p.m. 1 Getting Started HW11 Read through this page carefully. You may typeset your homework in latex or submit neatly handwritten/scanned solutions. Please start each question on a new page. Deliverables: 1. Submit a PDF of your writeup,
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EECS 189 Introduction to Machine Learning Fall 2020 This homework is due Tuesday, September 15 at 11:59 p.m. 2 Nonlinear Classification Boundaries HW2 Make sure to submit the code you write in this problem to “HW2 Code” on Gradescope. In this problem we will learn how to use polynomial features to learn nonlinear classification boundaries.
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COMP6714: Informa2on Retrieval & Web Search Introduc)on to Informa(on Retrieval Lecture 1: Boolean retrieval COMP6714: Informa2on Retrieval & Web Search Sec. 1.1 Unstructured data in 1680 Which plays of Shakespeare contain the words Brutus AND Caesar but NOT Calpurnia? One could grep all of Shakespeare’s plays for Brutus and Caesar, then strip out
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COMP6714: Information Retrieval & Web Search Introduction to Information Retrieval Lecture 4: Index Construction 1 COMP6714: Information Retrieval & Web Search Plan ▪ Last lecture: ▪ Dictionary data structures ▪ Tolerant retrieval ▪ Wildcards ▪ Spell correction ▪ Soundex ▪ This time: ▪ Index construction a-hu hy-mn-z $m mace madden mo among amortize on abandon
COMP6714 Lecture 1 Information Retrieval and Search Engines Lecturer: Wei Wang Date: 1 Galloping Search 1.1 Description Algorithm 1: skipTo(x) 1 2 3 4 5 6 7 8 9 Data: cur is the position of the current docID under the cursor in the posting list L. L[pos] returns the docID at postion pos. /* Stage
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COMP6714: Information Retrieval & Web Search Introduction to Information Retrieval Lecture 5: Index Compression 1 COMP6714: Information Retrieval & Web Search Inverted index ▪For each term t, we must store a list of all documents that contain t. ▪ Identify each doc by a docID, a document serial number ▪Can we used fixed-size arrays for
1 2 2.1 COMP6714 Project Specification (stage 2) October 4, 2018 COMP6714 18s2 Project Stage 2: Modify a baseline model of hyponymy classification Deadline and Late Penalty The project deadline is 23:59 26 Oct 2018 (Fri). Late penalty is -10% each day for the first three days, and then -20% each day afterwards. 2.2 Ob
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COMP6714 ASSIGNMENT 1 DUE ON 20:59 4 NOV, 2020 (WED) Q1. (25 marks) Some Boolean retrieval systems (e.g., Westlaw) support the proximity operator /S, which restricts the occurrences matches to be within the same sentence. Assume that we have created an additional positional list for $, which records the posi- tions of the end of
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