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CS-526
17 - 18 February
Notes Rodrigo Veiga I
Notes Rodrigo Veiga I
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lt_notes_pac_learning_RV1.pdf
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◄ Discussion and questions
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Discussion and questions
Notes Rodrigo Veiga II
PAC learning framework (notes N.M)
Finite classes and uniform convergence (notes N.M)
solution 1
No free lunch theorem
extrahmw-Hoeffding.pdf
solution 2
Learning infinite classes I
Homework 3 graded
Submission for HW3
solution-hmw-3-graded
Learning infinite classes II (VCdim)
Part I - Bias Viariance Decomposition and Double Descent
Moore Penrose hmw
Moore Penrose solution
Homework 4
Solution Homework 4
Additional exercises on PAC learning and VC dimension
Solution to Extra HW
solution hmw 4
Part II - Double descent phenomenon and derivation for the weak features model
Two models of weak features by Belkin, Hsu, Xu
Homework 5
Homework 5 solution
Gradient descent
Stochastic gradient descent
homework 6 graded
Submission for HW6
solution hmw 6 graded
Mean field approach for two layer NNs part I
Mean field approach to two layer NNs part II
Lecture on two layer neural networks (by Andrea Montanari)
homework 7
Solution 7
tensor lect 1
tensor lect 1 cont
homework 8 -graded
solution 8 - graded
Submission for HW8
tensor lect 2
tensor lect 2 cont
Homework 9
Solutions hmw 9
tensor lect 3
tensor lect 3 cont
tensor lect 4
tensor lect 4 cont
Homework 10
Solution hmw 10
tensor lect 5
tensor lect 5 cont
exam 2021
exam 2022
exam 2023
exam 2024
Notes Rodrigo Veiga II ►
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