├── AMP-tuto ├── From the SK model to matrix factorization.ipynb ├── Perceptron.ipynb ├── README.md ├── Sparse linear regression and compressed sensing.ipynb ├── Untitled.ipynb ├── exo1.py ├── exo2.py ├── exo3.py ├── exo4.py ├── exo5.py └── img │ └── cluster.png ├── DUKE.pdf ├── Screenshot 2019-03-18 at 16.26.19.png ├── _config.yml ├── exercices ├── Assignment0 Solution.pdf ├── Assignment0 Solution.tex ├── Assignment1 Solution.pdf ├── Assignment1 Solution.tex ├── Assignment2 Solution.pdf ├── Assignment2 Solution.tex ├── Assignment3 Solution.pdf ├── Assignment3 Solution.tex ├── Assignment4 Solution.pdf ├── Assignment4 Solution.tex ├── Assignment5 Solution.pdf ├── Assignment5 Solution.tex ├── hw1 │ ├── HW1.ipynb │ ├── hw1_2(a)_1.pdf │ ├── hw1_2(a)_2.pdf │ ├── hw1_2(b)_1,2.pdf │ ├── hw1_2(b)_3.pdf │ ├── hw1_2(c).pdf │ ├── hw1_3(a).pdf │ └── hw1_3(b).pdf ├── hw2 │ ├── .ipynb_checkpoints │ │ └── HW2-checkpoint.ipynb │ ├── HW2.ipynb │ ├── hw2_1(b).pdf │ ├── hw2_1(c).pdf │ ├── hw2_2(b)1.pdf │ ├── hw2_2(b)2.pdf │ ├── hw2_3(c).pdf │ ├── hw2_3(d).pdf │ ├── k_factor_ER_simul.npz │ └── k_factor_ER_simul.py ├── hw3 │ ├── .ipynb_checkpoints │ │ ├── HW2-checkpoint.ipynb │ │ └── HW3-checkpoint.ipynb │ ├── HW3.ipynb │ ├── coloring_ER_simul.npz │ ├── coloring_ER_simul.py │ ├── hw3_1(b).pdf │ ├── hw3_1(c).pdf │ ├── hw3_1(d).pdf │ ├── hw3_2(b).pdf │ └── hw3_2(b)2.pdf ├── hw4 │ ├── .ipynb_checkpoints │ │ ├── HW2-checkpoint.ipynb │ │ ├── HW3-checkpoint.ipynb │ │ └── HW4-checkpoint.ipynb │ ├── HW4.ipynb │ ├── hw4_1(b)1.pdf │ ├── hw4_1(b)2.pdf │ ├── hw4_2.pdf │ ├── hw4_3(a)1.pdf │ ├── hw4_3(a)2.pdf │ ├── hw4_3.pdf │ ├── hw4_5_1.pdf │ ├── hw4_5_2.pdf │ ├── spectral.py │ ├── spectral_rho=0.075.npz │ └── spectral_rho=0.5.npz ├── hw5 │ ├── .ipynb_checkpoints │ │ ├── HW2-checkpoint.ipynb │ │ ├── HW3-checkpoint.ipynb │ │ ├── HW4-checkpoint.ipynb │ │ └── HW5-checkpoint.ipynb │ ├── HW5.ipynb │ ├── hw5_3(c)1.pdf │ ├── hw5_3(c)2.pdf │ ├── planted_graph_coloring_result_q=3_N=10000 (1).npz │ ├── 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