| Date
| Assignments
| Topic
| Readings and Worksheets
| Quizzes
|
|---|
|
| ----
|
| Review Probability and calculus (topics)
| Bishop 1.1-1.4, probability intro slides, MIT Probability Open Course
| self-test
|
| ----
|
| Review Linear Algebra (topics)
| See Resources, MLMath
| self-test
|
|
| ----
|
| Review python, numpy, jupyter, colab
| See Resources
|
|
|
| W/Sep 2
|
| Introduction (slides)
|
| quiz
|
| F/Sep 4
|
| Background: colab, pandas (slides)
| Worksheets: Python for ML, regression in python, array practice
| survey
|
|
| M/Sep 7
|
| No class: Labor Day
|
|
|
| W/Sep 9
|
| Local Learning and Decision Trees (slides)
| decision trees, info theory; Supplemental: Decision tree viz
Worksheets: K-NN, Decision trees, info gain
| quiz
|
| F/Sep 11
|
| MLE/MAP and Gaussians (pdf slides)
| Background: Gaussians
Worksheet: MLE/MAP
| quiz and regression prequiz, survey
|
|
| M/Sep 14
| HW 0 due
| Linear Regression
(slides)
| Bishop 3.1-3.3
Worksheet: Regression with MLE/MAP
| quiz
|
| W/Sep 16
|
| Overfitting and regularization; Bias-Variance decomposition
| Worksheet: Bias/Variance
| quiz
|
| F/Sep 18
|
| Generalized Linear Regression;Questions
|
| quiz,survey
|
|
| M/Sep 21
| HW 1 due
| Regression Penalties and Gradient Descent
| Worksheets: Regression Penalties, gradient descent
| quiz1, quiz2
|
| W/Sep 23
|
| Logistic (slides) and Kernel Regression (slides)
| Supplemental: Bishop 4.0-4.5; Local Learning: Bishop 2.5 Worksheet: LR
| quiz1,quiz2
|
| F/Sep 25
|
| Scale Invariance; KL-divergence; Review
|
| quiz; survey
|
|
| M/Sep 28
| HW 2 due
| Neural Nets A
| stanford CNN course Supplemental: PyTorch
Worksheet: NNs
| quiz
|
| W/Sep 30
|
| Neural Nets B
| Worksheet: CNNs
| quiz
|
| F/Oct 2
|
| GANs
| Worksheet: GANs; Supplemental (for fun): Ian Goodfellow on GANS
| quiz;survey
|
|
| M/Oct 5
| HW 3 due
| Boosting, Random Forests (slides, r.e. midterm)
| Bishop 14.3; Supplemental: shapire tutorial
Worksheet: Boosting
| quiz
|
| W/Oct 7
|
| Support Vector Machines (SVMs) (slides)
| Bishop on large margin
Worksheet: SVM
| quiz
|
| F/Oct 9
|
| Review Session (slides)
|
| survey
|
|
| M/Oct 12
| HW 4 due
| Online Learning
| Supplemental: Perceptron proof, Bishop on perceptrons
| quiz
|
|
|
| W/Oct 14
|
| Midterm
| 2018 midterm and solutions; 2019 midterm and solutions and 2019 midterm comments
|
|
| F/Oct 16
|
| SVD (slides)
| SVD wikipedia; Background: matrices, Bishop, and 3blue1brown videos
Worksheet: Matrix in Python SVD
| quiz, survey
|
|
| M/Oct 19
|
| PCA (slides)
| PCA from Bishop
Worksheet: PCA
| quiz
|
| W/Oct 21
|
| PCA uses
|
| quiz
|
|
|
| F/Oct 23
|
| midterm comments
|
| no quiz or survey
|
|
| M/Oct 26
|
| Clustering (slides) and EM
| Bishop Ch 9
Worksheet: kmeans
| kmeans quiz, GMM quiz
|
| W/Oct 28
|
| EM and Missing Data (slides)
| Worksheet: missing data
| EM quiz, missing quiz
|
| F/Oct 30
|
| Imputation (slides)
|
| survey
|
|
| M/Nov 2
| HW 5 due
| Netflix (slides)
| Supplemental: netflix winning paper Worksheet:Recommender Systems
| quiz
|
| W/Nov 4
|
| Real world ML; (slides)
| Worksheet: Evaluation Metrics
data sources
| quiz
|
| F/Nov 6
|
| Project advice, slides Other ML courses
| visualization
Worksheet: WS: Interpreting coefficients
| survey
|
|
| M/Nov 9
| HW 6 due
| Naive Bayes (slides), and LDA (slides)
| Worksheet: NB/LDA; Supplemental: LDA intro
| NB quiz;LDA quiz
|
| W/Nov 11
|
| Belief Nets (slides), HMMs (slides)
| Worksheet: Belief Net; HMM
| quiz
|
| F/Nov 13
|
| Belief Nets and friends
|
| survey
a
|
| M/Nov 16
| Project proposal due
| Recurrent neural nets (slides)
| Worksheet: RNN; Supplemental: GPT-3 outputs
| quiz
|
| W/Nov 18
|
| Reinforcement Learning I (slides)
| Worksheet: RL, Supplemental: RL-intro, MDPs, MDP-2, and MDP-3
| quiz
|
| F/Nov 20
|
| RL recitation
|
| survey
|
|
| M/Nov 23
| HW 7 due
| RL: DQN and alphaZero (slides)
| Supplemental:SARSA, RL for starcraft
| quiz
|
| W/Nov 25
|
| No class but you can schedule a meeting to discuss your project
|
|
|
| F/Nov 27
|
| No class: Thanksgiving
|
|
|
|
| M/Nov 30
| Project checkpoint due
| Autoencoders and AutoML
| ICA. Worksheets: Autoencoders and AutoML
| quiz1, quiz2
|
| W/Dec 2
|
| Active Learning (slides)
| Worksheet: active learning
| quiz
|
| F/Dec 4
|
| Recitation (slides)
|
| final survey
|
|
| M/Dec 7
| HW 8 due
| Interpretation and Causality (slides) Bias
|
| quiz, bias quiz
|
| W/Dec 9
|
| Course Review (slides)
| Supplemental: tesla ML video
| quiz
|
| R/Dec 10
| Project due
| No class
|
| project participation
|
|
| M/Dec 14
|
| Extra review session 10:30 am (usual Monday zoom)
| (slides)
|
|
|
| W/Dec 16
|
| Final 24 hours, starting and ending at 10:00 am ET
| 2018 final and solutions; 2019 final and solutions
|
|
|
|
