Jump to:
[Unit 1: Regression] - [Unit 2: Classification] - [Unit 3: Neural Nets]
[Unit 4: Trees and Ensembles] - [Unit 5: Kernels] - [Unit 6: PCA and Rec. Sys.] - [Unit 7: Frontiers]
Please complete assigned readings and videos before the start of class.
Schedule might change slightly as the semester goes on. Please check here regularly and refresh the page. Look for key announcements on Piazza.
Course Introduction
Concepts: supervised learning, unsupervised learning, difference between ML and AI
Date | Assignments | Do Before Class | Class Content | Optional Extras |
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Wed 09/09 day01 |
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Unit 1: Regression
Concepts: over-fitting, under-fitting, cross-validation
Methods: Linear regression, k-NN regression
Evaluation: mean squared error, mean absolute error
Work: HW1, Quiz1
Date | Assignments | Do Before Class | Class Content | Optional Extras |
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Mon 09/14 day02 |
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Wed 09/16 day03 |
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Mon 09/21 day04 |
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Wed 09/23 day05 |
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Unit 2: Classification
Concepts: feature engineering, hyperparameters, numerical stability, gradient descent
Methods: Logistic regression, k-NN classification
Evaluation: ROC curves, confusion matrices, cross entropy
Work: HW2, Quiz2, ProjectA
Date | Assignments | Do Before Class | Class Content | Optional Extras |
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Mon 09/28 day06 |
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Wed 09/30 day07 |
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Mon 10/05 day08 |
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Wed 10/07 day09 |
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Unit 3: Neural Nets
Concepts: backpropagation, stochastic gradient descent, hyperparameter selection
Methods: multi-layer perceptrons for regression and classification
Work: HW3, Quiz3
Date | Assignments | Do Before Class | Class Content | Optional Extras |
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Mon 10/12 day10 |
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Wed 10/14 day11 |
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Mon 10/19 day12 |
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Wed 10/21 day13 |
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Unit 4: Trees and Ensembles
Concepts: greedy training, bagging, boosting
Methods: decision trees, random forests, XGBoost
Work: HW4, Quiz4, ProjectB
Date | Assignments | Do Before Class | Class Content | Optional Extras |
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Mon 10/26 day14 |
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Wed 10/28 day15 |
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Mon 11/02 day16 |
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Wed 11/04 day17 |
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Unit 5: Kernel Methods
Concepts: kernel functions
Methods: kernelized linear regression, support vector machines
Work: HW5, Quiz5
Date | Assignments | Do Before Class | Class Content | Optional Extras |
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Mon 11/09 day18 |
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Tue 11/10 (Tufts-Wed-on-Tues) day19 |
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Wed 11/11 |
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NO CLASS (Veteran's Day Holiday) | ||
Mon 11/16 day20 |
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Wed 11/18 day21 |
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Unit 6: PCA and Recommender Systems
Concepts: dimensionality reduction, matrix factorization, recommendation systems
Methods: principal components analysis, collaborative filtering models
Work: ProjectC
Date | Assignments | Do Before Class | Class Content | Optional Extras |
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Mon 11/23 day22 |
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Wed 11/25 | NO CLASS (Thanksgiving Holiday) | |||
Mon 11/30 day23 | ||||
Wed 12/02 day24 |
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Unit 7: ML Frontiers
Date | Assignments | Do Before Class | Class Content | Optional Extras |
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Mon 12/07 day25 |
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Wed 12/09 |
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Mon 12/21 |
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NO CLASS (Final Exam Period) |