HW5
- Due Nov 13, 2020 by 11:59pm
- Points 100
- Submitting a file upload
Homework Project 5
- Due Fri, Nov 13, 2020
- Homeworks, projects and assignments
- Homework Submission Rules
- Homework Headers
Problem 5.1 (Learning the Power Law):
Design, train, and optimize a generic neural network (NN) that can learn and predict the power-function
Links to an external site. for a given power parameter (λ∈R). Assess the accuracy of the NN prediction of the power function. [Hint: Recall the example with the square-root function
Links to an external site..]
Problem 5.2 (ALS Clustering):
Use the ALS dataset to study a rare but devastating progressive neurodegenerative disease, amyotrophic lateral sclerosis (ALS). Major clinically relevant questions include: What patient phenotypes can be automatically and reliably identified and used to predict the change of the ALSFRS slope over time?
- Load and prepare the data
- Report (short!) data summaries and show some preliminary visualizations
- Train a k-Means model on the data, select k
- Evaluate the model performance using bar and silhouette plots and summarize the results
- Tune and plot parameters with k-means++
- Rerun the model with the optimal parameters and interpret the clustering results
- Apply Hierarchical Clustering on three different linkages and compare the corresponding silhouette plots
- Fit a Gaussian mixture model, select the optimal model, report BIC, and display density and classification plots
- Compare the result of the above methods
Note: Some of the most clinically important ALS features to focus on include:
Age_mean; ALSFRS_slope; ALSFRS_Total_max; ALSFRS_Total_median; ALSFRS_Total_range; Blood.Urea.Nitrogen..BUN._median; Blood.Urea.Nitrogen..BUN._min; Blood.Urea.Nitrogen..BUN._range; BMI_max; bp_diastolic_median; bp_systolic_median; Calcium_median; Chloride_median; Creatinine_median; Glucose_median; Hemoglobin_median; eg_median; leg_min; leg_range; Lymphocytes_median; mouth_median; onset_delta_mean; onset_site_mean; respiratory_median; Sodium_median; trunk_max; trunk_median;
Rubric
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Correctness and scientific validity
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Result reproducibility
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Content focus, presentaiton style, and clarity
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Total Points:
100
out of 100
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