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Carnegie Mellon University
95-828 Machine Learning for Problem Solving
Spring 2021

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Course Syllabus (download as pdf)

LECTURES:

I will provide course notes as well as slides for each lecture. Those will be uploaded to Canvas before the lecture. Feel free to print them and bring them to class with you for annotating.

You may also benefit from the recommended books (listed under Resources, see left tab) to further your understanding. To stay on track, make sure to read the course notes in a timely fashion, and follow up with questions in lectures, office hours, recitations, and/or Piazza.

RECITATIONS:

There will be a recitation session held by one of the TAs on Fridays. The recitation will review the week's material and answer any questions you might have about the course material, including homework.

Week

Lectures

Notes

Week 1

INTRO TO MACHINE LEARNING     [+]

HW 0 out • Python and Jupyter setup

DATA PREPARATION     [+]

Recitation 1 • Python setup • Data prep

PART I: SUPERVISED LEARNING

Week 2

LINEAR REGRESSION (LR)     [+]

Recitation 2 Data prep demos • Linear Algebra review

Week 3

MODEL SELECTION     [+]

Recitation 3 • Linear Reg. demos • Convex optimization basics

HW 1 out • EDA • LR • Model selection • LogR

Week 4

LOGISTIC REGRESSION (LogR)     [+]

Recitation 4 Bias-Variance trade-off • Cross-validation

Week 5

Week 6


NON-PARAMETRIC LEARNING     [+]


MODEL EVALUATION     [+]

Recitation 5 LogR • Gradient descent review and demos

HW 2 out • Non-parametric learning • Model evaluation • DT

Recitation 6 • kNN • Kernel regression • Model evaluation

Week 7

DECISION TREES (DT)     [+]

Recitation 7 • DT review and demos

Week 8

Midterm Review

Midterm Exam

Exam will be during class on Thur. Duration: 80 minutes. You can only bring your own notes up to 2 A4-size sheets. No electronics.

Friday NO RECITATION

Week 9

NO CLASS: Spring Break


Week 10

ENSEMBLE METHODS     [+]


NAIVE BAYES (NB)     [+]

HW 3 out • Ensembles • NB • SVM

Recitation 8 Random Forest • Boosting • NB

Case Study out • Dataset provided, Tasks recommended

Week 11

SUPPORT VECTOR MACHINES (SVM)     [+]



Recitation 9 • SVM and Kernels

Week 12

NEURAL NETWORKS (NN)     [+]

HW 4 out • Kernels • Neural Nets • Density estimation

Recitation 10 • NNs • Back-propagation

Week 13


PART II: UNSUPERVISED LEARNING

DENSITY ESTIMATION     [+]



Thur NO CLASS: Spring carnival

Friday NO RECITATION

Week 14


Week 15

CLUSTERING     [+]


DIMENSIONALITY REDUCTION     [+]

HW 5 out • Clustering • EM • Dimensionality reduction

Recitation 11 • Density estimation • hierarchical clustering • k-means

Recitation 12 • EM • Dim. reduction

Week 16

Case Study & Final Review

Recitation 13 • Case Study review • Final Q&A