Lectures: Towne 100 (Heilmeier Hall), Monday and Wednesday: 10:30am-noon, Recitation:Friday: 9:30am-11:00am
See canvas for lecture recordings; you can also download them.
Lectures and homework dates subject to change; Midterm and final dates are not
| Date | Assignments | Topic | Readings | Worksheets/Quizzes | |
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| —— | Probability Review | Bishop 1.1-1.4; probability intro slides; MIT Probability Open Course | self-test | ||
| —— | Review Linear Algebra | See Resources, MLMath | self-test | ||
| —— | Review python, numpy, jupyter | See Resources | |||
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| W/Aug 28 | Introduction,(slides) | quiz | |||
| F/Aug 30 | Tutorial: Python for ML | regression in python | |||
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| M/Sep 2 | No class: Labor Day | ||||
| W/Sep 4 | Local Learning and Decision Trees (slides) | decision trees, info theory, supplemental: Decision trees, visually | quiz | ||
| F/Sep 6 | Tutorial: MLE/MAP point estimates and Gaussians | background: Gaussians | quiz and prequiz for Regression | ||
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| M/Sep 9 | HW 0 due | Least Squares Regression
(slides) and (Gradient Descent) | Bishop 3.1-3.3 | quiz | |
| W/Sep 11 | Overfitting and regularization; Bias-Variance decomposition | quiz | |||
| F/Sep 13 | Discussion: Linear Regression | ||||
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| M/Sep 16 | HW 1 due | Regression Penalties and Gradient Descent | quiz1,quiz2 | ||
| W/Sep 18 | Logistic (pdf) and kernel Regression (pdf) | supplemental:Bishop 4.0-4.5, local learning: Bishop 2.5 | quiz1,quiz2 | ||
| F/Sep 20 | Review: Kernel functions, positive definite, basis… | Scale Invariance | |||
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| M/Sep 23 | HW 2 due | Neural Nets | stanford CNN course | quiz | |
| W/Sep 25 | CNNs and limitations | quiz | |||
| F/Sep 27 | GANs and KL-divergence | supplemental (for fun):Ian Goodfellow on GANS | quiz | ||
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| M/Sep 30 | HW 3 due | Boosting, (slides) | bishop 14.3 suppplemental: shapire tutorial | quiz | |
| W/Oct 2 | Support Vector Machines (slides) | Bishop on large margin | quiz | ||
| F/Oct 4 | No class | ||||
| Sun/Oct 6 3:00 PM | Levine Hall Wu & Chen Auditorium | Review Session, (slides) | quiz | ||
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| M/Oct 7 | HW 4 due | Online Learning | supplemental Perceptron proof | quiz | |
| W/Oct 9 | Midterm | 2018 midterm and solutions | |||
| F/Oct 11 | No class: Fall Break | ||||
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| M/Oct 14 | SVD (slides) | SVD wikipedia; background: matrices, Bishop, and 3blue1brown videos | quiz | ||
| W/Oct 16 | PCA (slides) | PCA from Bishop | quiz | ||
| F/Oct 18 | midterm; eigenwords | ||||
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| M/Oct 21 | Clustering | bishop ch 9 | quiz | ||
| W/Oct 23 | EM and Missing Data (slides) | ||||
| F/Oct 25 | slides | ||||
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| M/Oct 28 | HW 5 due | Netflix(slides) | supplemental: netflix winning paper | quiz | |
| W/Oct 30 | Evaluation; | data sources | quiz | ||
| F/Nov 1 | Project advice ; Real world ML | Other ML courses; Interpreting coefficents | |||
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| M/Nov 4 | HW 6 due | Naıve Bayes (slides), and LDA (slides) | Supplemental:LDA intro | NB quiz;LDA quiz | |
| W/Nov 6 | Belief Nets (slides), HMMs (slides) | ||||
| F/Nov 8 | Belief Nets and friends | ||||
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| M/Nov 11 | Project proposal | Recurrent neural nets (slides) | talk to transformer | quiz | |
| W/Nov 13 | Reinforcement Learning I (slides) | supplemental: RL-intro SARSA | |||
| F/Nov 15 | Discussion: RL,visualization | ||||
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| M/Nov 18 | HW 7 due | Reinforcement Learning II | supplemental: MDPs, MDP-2, and MDP-3 | ||
| W/Nov 20 | RL III; DQN; alphaGo; alphaZero (slides) | RL quiz | |||
| F/Nov 22 | RL review | Supplemental: RL for starcraft | |||
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| M/Nov 25 | Autoencoders and AutoML | ICA | quiz1, quiz2 | ||
| W/Nov 27 | No class but you can schedule a meeting to discuss your project | ||||
| F/Nov 30 | No class: Thanksgiving | ||||
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| M/Dec 2 | Active Learning (slides) | workbook | quiz | ||
| W/Dec 4 | HW 8 due | Bias, Causality | quiz | ||
| F/Dec 6 | No class - work on your project | ||||
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| M/Dec 9 | Project due | Course Summary | supplemental:summary, tesla ML video | ||
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| W/Dec 18 | Review session 3:00 pm | Wu and Chen | slides and audio | quiz | |
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| R/Dec 19 | Final 9:00–11:00 am | Final! David Rittenhouse Labs (DRL) A1 | 2018 final and solutions | ||