Assessment 2 Weighting: 45% Total marks: 40 The assessment covers the content of Week 3-6. It addresses the following learning outcome(s): • Analyse real world tasks using multi-layer perceptron neural network, ARMA/ARIMA and LSTM for classiIication and time-series prediction. • Develop and deploy multi-layer perceptron neural network, ARMA/ARIMA and LSTM in Python • Tune hyperparameters …
CS/ECE/STAT-861: Theoretical Foundations of Machine Learning University of Wisconsin–Madison, Fall 2023 Homework 2. Due 10/27/2023, 11.00 am Instructions: 1. Homework is due at 11 am on the due date. Please hand over your homework at the beginning of class. Please see the course website for the policy on late submission. 2. I recommend that you …
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CS/ECE/STAT-861: Theoretical Foundations of Machine Learning University of Wisconsin–Madison, Fall 2023 Homework 0. Due 9/15/2023, 11.00 am Instructions: 1. Homework is due at 11 am on the due date. Please hand over your homework at the beginning of class. Please see the course website for the policy on late submission. 2. I recommend that you …
STAT 861: Theoretical Foundations of Machine Learning University of Wisconsin–Ma Read More »
CS/ECE/STAT-861: Theoretical Foundations of Machine Learning University of Wisconsin–Madison, Fall 2023 Homework 1. Due 10/06/2023, 11.00 am Instructions: 1. Homework is due at 11 am on the due date. Please hand over your homework at the beginning of class. Please see the course website for the policy on late submission. 2. I recommend that you …
STAT 861: Theoretical Foundations of Machine Learning University of Wisconsin–Ma Read More »
CS/ECE/STAT-861: Theoretical Foundations of Machine Learning University of Wisconsin–Madison, Fall 2023 Homework 3. Due 11/08/2023, 11.00 am Instructions: 1. Homework is due at 11 am on the due date. Please hand over your homework at the beginning of class. Please see the course website for the policy on late submissions. 2. I recommend that you …
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CS861: Theoretical Foundations of Machine Learning Lecture 5 – 09/15/2023 University of Wisconsin–Madison, Fall 2023 Lecture 05: Growth Function and VC Dimension Lecturer: Kirthevasan Kandasamy Scribed by: Chenghui Zheng, Yixuan Zhang Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications. They may be distributed outside this class only with …
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CS861: Theoretical Foundations of Machine Learning Lecture 22 – 10/25/2023 University of Wisconsin–Madison, Fall 2023 Lecture 22: Online learning, The experts problem Lecturer: Kirthevasan Kandasamy Scribed by: Xinyan Wang, Zhifeng Chen Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications. They may be distributed outside this class only with …
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CS861: Theoretical Foundations of Machine Learning Lecture 6 – 09/18/2023 University of Wisconsin–Madison, Fall 2023 Lecture 06: PAC bound in a finite VC class, Proof of Sauer’s lemma Lecturer: Kirthevasan Kandasamy Scribed by: Justin Kiefel, Joseph Salzer Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications. They may be …
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CS861: Theoretical Foundations of Machine Learning Lecture 26 – 03/11/2023 University of Wisconsin–Madison, Fall 2023 Lecture 26: Online Convex Optimization (continued) Lecturer: Kirthevasan Kandasamy Scribed by: Haoyue Bai & Deep Patel Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications. They may be distributed outside this class only with …
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CS861: Theoretical Foundations of Machine Learning Lecture 10 – 27/09/2023 University of Wisconsin–Madison, Fall 2023 Lecture 10: Le Cam’s Method (Some Examples) Lecturer: Kirthevasan Kandasamy Scribed by: Ying Fu, Deep Patel Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications. They may be distributed outside this class only with …
CS861: Theoretical Foundations of Machine Learning Lecture 10 27 09 2023 Unive Read More »