# Lectures

# Schedule

Week# | Date | Title | |||
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Week 1 | Jan 17 | Course outline and Overview of Mini Projects - Slides | |||

a) Computer System Analytics - Blue Waters System Monitoring | |||||

b) Healthcare analytics - Genomics/Cancer Example | |||||

c) Healthcare analytics – Neuroscience Example | |||||

Resilience of large-scale systems |
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Week 2 | Jan 22 | Lecture 1 : Lecture 1: Probability Basics Overview (Recorded Lecture) - Slides |
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Jan 24 | Lecture 2 : Greg Bauer’s lecture on monitoring Blue Waters, 13.3 PF supercomputer - Slides |
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Implementing end-to-end workflow for resiliency analysis |
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(i) Data management for resiliency logs, selection of feature pruning methods, dimensionality reduction methods | |||||

(ii) Domain component: Reliability/availability modeling, Dependent and common-mode failures and their characterizations | |||||

(iii) Introduction to LogDiver toolchain for error-data acquisition and preliminary filtering of Blue Waters datasets | |||||

Details |
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(i) Raw measurements to initial features using coalescing techniques (temporal reduction and data management) | |||||

(ii) Dimensionality reduction (Spatial reduction of dataset, PCA, correlation techniques, and sensitivity analysis) | |||||

(iii) Applying joint probabilities and conditional/marginal probabilities to understand dependent failures | |||||

Week 3 | Jan 29 | Lecture 1 : Failure/Reliability/Availability of Compute System, Series-Parallel Systems, ECC - Slides |
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Jan 31 | Lecture 2 : Chipkill, Importance of Filtering in Data Analysis, Coalescing techniques, Mini-project 1 discussion & release - Slides |
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Describing project and related analytical methods: | |||||

1. Measuring reliability and availability of Blue Waters system and applications | |||||

2. Diagnosing the cause of failures | |||||

Week 4 | Feb 5 | Lecture 1 : Introduction to Bayesian Networks - Slides |
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Feb 7 | Mini Project 1 | ||||

Lecture 2 : Bayesian Networks - Slides |
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Week 5 | Feb 12 | Lecture 1 : In-class activity on Bayesian Networks - Homework 2 and In Class Activity |
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Feb 14 | Lecture 2 : In-class lab - Notebook |
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Healthcare analytics: Quantifying drug response and disease progression |
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(i) Data management for genomics and related health-records capturing the uniqueness of these health-records | |||||

(ii) Data filtering, feature selection, fitting distributions, selection of clustering methods (GMMs, K-means), longitudinal data analysis | |||||

(iii) Domain component: gene expression, dependence between gene expression and genetic diseases (in particular breast cancer), understanding diseases progression | |||||

(iv) Introduction to MiMoSA toolchain and publicly available breast cancer dataset | |||||

Details: |
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(i) Raw measurements to initial features using sensitivity analysis | |||||

(ii) Identifying data modalities based on the source of the dataset and the demographics represented in the dataset | |||||

(iii) Using k-means and GMM's for clustering on processed dataset for understanding gene expression. Developing insights into the understanding the difference between these clustering methods | |||||

(iv) Using developed framework to understand disease progression | |||||

Week 6 | Feb 19 | Lecture 1: Introduction to health-care domain: disease models, drug response, forecasting disease progression - Slides |
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Feb 21 | Lecture 2: MP1 In Class Presentations |
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Week 7 | Feb 26 | Lecture 1: Data filtering, feature selection, fitting distributions, selection of clustering methods (GMMs, K-means, Linear Regression) - Slides |
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Feb 28 | Lecture 2: Clustering - Hierarchical Clustering - Slides |
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Week 8 | Mar 5 | Lecture 1: In Class Activity 2 |
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Mar 7 | Lecture 2: Principal Component Analysis, Guest Lecture by a Mayo clinician from Center for Individualized Medicine - Slides |
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Mar 10 | Midterm Review - Slides | ||||

Week 9 | Mar 12 | Lecture 1: MIDTERM - Rubric |
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Mar 14 | Lecture 2: In Class Lab on PCA - Notebook + Datasets |
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Autonomous Security Monitoring for Enterprise Systems: Data-driven learning and inference |
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Week 10 | Mar 26 | Lecture 1: Midterm Discussion - Slides |
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Mar 28 | Lecture 2: Introduction to Factor Graphs - Slides |
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Week 11 | Apr 2 | Lecture 1: Factor Graphs - Slides |
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Apr 4 | Lecture 2: Pair HMMs, Factor Graphs - Slides |
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Week 12 | Apr 9 | Lecture 1: HW5 Solution and In Class Activity - Slides |
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Apr 11 | Lecture 2: In-class lab on mini-project 3 - Slides HMM Code |
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Week 13 | Apr 16 | Lecture 1 : Factor Graphs - Slides |
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Apr 18 | Lecture 2 : In-Class Activity on Factor Graphs |
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Week 14 | Apr 23 | Lecture 1 : Classification and Neural Networks - Slides |
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Apr 25 | Lecture 2: In-Class Lab on MP3 - ZIP |
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Week 15 | Apr 30 | Lecture 1 : Group Activity - Solutions |
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Mat 2 | Lecture 2: Review - Slides |
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FINAL | Wednesday, May 9, 7-10pm | As per the exam calendar |