10-301 / 10-601
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Course Schedule

Open latest poll (Lecture 27)
Date
CategoryTopicStatusContext
1
Mon, Jan 12
Homeworks
Mon, Jan 12Homework 1: Background MaterialModule: Classification & Regression
RELEASED
Machine Learning as Function Approximation
Lectures
Mon, Jan 12Lecture 1: Course OverviewModule: Classification & Regression
Wed, Jan 14
Lectures
Wed, Jan 14Lecture 2: Machine Learning as Function ApproximationModule: Classification & Regression
Fri, Jan 16
Coding Labs
Fri, Jan 16Coding Lab: HW1Module: Classification & Regression
Machine Learning as Function Approximation
2
Wed, Jan 21
Lectures
Wed, Jan 21Lecture 3: Decision TreesModule: Classification & Regression
Homeworks
Wed, Jan 21Homework 2: Decision TreesModule: Classification & Regression
RELEASED
Decision Tree Properties and Hyperparameters
Homeworks
Wed, Jan 21Homework 1: Background Material (Slot A)Module: Classification & Regression
DUE
Machine Learning as Function Approximation
Fri, Jan 23
Coding Labs
Fri, Jan 23Coding Lab: HW2Module: Classification & Regression
Decision Trees
3
Mon, Jan 26
Lectures
Mon, Jan 26Lecture 4: Decision Tree Properties and HyperparametersModule: Classification & Regression
Wed, Jan 28
Lectures
Wed, Jan 28Lecture 5: k-Nearest Neighbor and Model SelectionModule: Classification & Regression
Homeworks
Wed, Jan 28Homework 1: Background Material (Slot B)Module: Classification & Regression
DUE
Machine Learning as Function Approximation
Fri, Jan 30
Lectures
Fri, Jan 30Lecture 6: PerceptronModule: Linear Models
4
Mon, Feb 2
Lectures
Mon, Feb 2Lecture 7: Linear RegressionModule: Linear Models
Homeworks
Mon, Feb 2Homework 3: KNN, Perceptron, and Linear RegressionModule: Linear Models
RELEASED
Homeworks
Mon, Feb 2Homework 2: Decision Trees (Slot A)Module: Classification & Regression
DUE
Decision Tree Properties and Hyperparameters
Wed, Feb 4
Lectures
Wed, Feb 4Lecture 8: Optimization for MLModule: Linear Models
Fri, Feb 6
Recitations
Fri, Feb 6Recitation: HW3Module: Linear Models
Linear Regression
Quizzes
Fri, Feb 6Quiz 1 (HW1/HW2)Module: Linear Models
Optimization for ML
Sun, Feb 8
Homeworks
Sun, Feb 8Homework 2: Decision Trees (Slot B)Module: Classification & Regression
DUE
Decision Tree Properties and Hyperparameters
5
Mon, Feb 9
Lectures
Mon, Feb 9Lecture 9: Stochastic Gradient Descent / Logistic RegressionModule: Linear Models
Homeworks
Mon, Feb 9Homework 3: KNN, Perceptron, and Linear Regression (Slot A)Module: Linear Models
DUE
Wed, Feb 11
Lectures
Wed, Feb 11Lecture 10: Feature Engineering / RegularizationModule: Linear Models
Sat, Feb 14
Homeworks
Sat, Feb 14Homework 3: KNN, Perceptron, and Linear Regression (Slot B)Module: Linear Models
DUE
6
Mon, Feb 16
Homeworks
Mon, Feb 16Homework 4: Logistic RegressionModule: Linear Models
RELEASED
Feature Engineering / Regularization
Lectures
Mon, Feb 16Lecture 11: Neural NetworksModule: Neural Networks
Exams
Mon, Feb 16Exam 1Module: Neural Networks
EXAM
Neural Networks
Wed, Feb 18
Lectures
Wed, Feb 18Lecture 12: Backpropagation IModule: Neural Networks
Fri, Feb 20
Coding Labs
Fri, Feb 20Coding Lab: HW4Module: Neural Networks
Backpropagation I
7
Mon, Feb 23
Lectures
Mon, Feb 23Lecture 13: Backpropagation IIModule: Neural Networks
Wed, Feb 25
Homeworks
Wed, Feb 25Homework 5: Neural NetworksModule: Neural Networks
RELEASED
Backpropagation II
Lectures
Wed, Feb 25Lecture 14: Societal Impacts of MLModule: Societal Impacts
Homeworks
Wed, Feb 25Homework 4: Logistic Regression (Slot A)Module: Linear Models
DUE
Feature Engineering / Regularization
Fri, Feb 27
Coding Labs
Fri, Feb 27Coding Lab: HW5Module: Societal Impacts
Societal Impacts of ML
9
Mon, Mar 9
Lectures
Mon, Mar 9Lecture 15: PAC learningModule: Learning Theory
Wed, Mar 11
Lectures
Wed, Mar 11Lecture 16: PAC Learning / MLE & MAPModule: Learning Theory
Thu, Mar 12
Homeworks
Thu, Mar 12Homework 4: Logistic Regression (Slot B)Module: Linear Models
DUE
Feature Engineering / Regularization
Fri, Mar 13
Recitations
Fri, Mar 13Recitation: HW6Module: Deep Learning
MLE & MAP / CNNs and RNNs
Sun, Mar 15
Homeworks
Sun, Mar 15Homework 6: Learning Theory and EthicsModule: Deep Learning
RELEASED
MLE & MAP / CNNs and RNNs
Homeworks
Sun, Mar 15Homework 5: Neural Networks (Slot A)Module: Neural Networks
DUE
Backpropagation II
10
Mon, Mar 16
Lectures
Mon, Mar 16Lecture 17: MLE & MAP / CNNs and RNNsModule: Deep Learning
Wed, Mar 18
Lectures
Wed, Mar 18Lecture 18: RNN-LMs and Transformers-LMsModule: Deep Learning
Fri, Mar 20
Quizzes
Fri, Mar 20Quiz 2 (HW4/HW5)Module: Deep Learning
RNN-LMs and Transformers-LMs
Sun, Mar 22
Homeworks
Sun, Mar 22Homework 5: Neural Networks (Slot B)Module: Neural Networks
DUE
Backpropagation II
Homeworks
Sun, Mar 22Homework 6: Learning Theory and Ethics (Slot A)Module: Deep Learning
DUE
MLE & MAP / CNNs and RNNs
11
Mon, Mar 23
Lectures
Mon, Mar 23Lecture 19: Transformers, AutoDiff, Pre-training, Fine-TuningModule: Deep Learning
Wed, Mar 25
Lectures
Wed, Mar 25Lecture 20: In-context Learning / Reinforcement Learning: MDPsModule: Reinforcement Learning
Thu, Mar 26
Homeworks
Thu, Mar 26Homework 7: Deep LearningModule: Deep Learning
RELEASED
Transformers, AutoDiff, Pre-training, Fine-Tuning
Exams
Thu, Mar 26Exam 2Module: Reinforcement Learning
EXAM
In-context Learning / Reinforcement Learning: MDPs
Fri, Mar 27
Coding Labs
Fri, Mar 27Coding Lab: HW7Module: Reinforcement Learning
In-context Learning / Reinforcement Learning: MDPs
Sun, Mar 29
Homeworks
Sun, Mar 29Homework 6: Learning Theory and Ethics (Slot B)Module: Deep Learning
DUE
MLE & MAP / CNNs and RNNs
12
Mon, Mar 30
Lectures
Mon, Mar 30Lecture 21: Reinforcement Learning: Value/Policy IterationModule: Reinforcement Learning
Wed, Apr 1
Lectures
Wed, Apr 1Lecture 22: Reinforcement Learning: Policy Gradient / Deep RLModule: Reinforcement Learning
Fri, Apr 3
Coding Labs
Fri, Apr 3Coding Lab: HW8Module: Reinforcement Learning
Reinforcement Learning: Policy Gradient / Deep RL
Sun, Apr 5
Homeworks
Sun, Apr 5Homework 8: Reinforcement LearningModule: Reinforcement Learning
RELEASED
Reinforcement Learning: Policy Gradient / Deep RL
Homeworks
Sun, Apr 5Homework 7: Deep Learning (Slot A)Module: Deep Learning
DUE
Transformers, AutoDiff, Pre-training, Fine-Tuning
13
Mon, Apr 6
Lectures
Mon, Apr 6Lecture 23: Recommender SystemsModule: Learning Paradigms
Wed, Apr 8
Lectures
Wed, Apr 8Lecture 24: Ensemble Methods: Boosting & BaggingModule: Learning Paradigms
14
Mon, Apr 13
Lectures
Mon, Apr 13Lecture 25: K-Means / Dimensionality Reduction: PCAModule: Learning Paradigms
Tue, Apr 14
Homeworks
Tue, Apr 14Homework 7: Deep Learning (Slot B)Module: Deep Learning
DUE
Transformers, AutoDiff, Pre-training, Fine-Tuning
Wed, Apr 15
Lectures
Wed, Apr 15Lecture 26: Special Topics: Coding Agents / Significance Testing for MLModule: Learning Paradigms
Thu, Apr 16
Homeworks
Thu, Apr 16Homework 9: Learning ParadigmsModule: Learning Paradigms
RELEASED
K-Means / Dimensionality Reduction: PCA
Homeworks
Thu, Apr 16Homework 8: Reinforcement Learning (Slot A)Module: Reinforcement Learning
DUE
Reinforcement Learning: Policy Gradient / Deep RL
Fri, Apr 17
Recitations
Fri, Apr 17Recitation: HW9Module: Learning Paradigms
K-Means / Dimensionality Reduction: PCA
15
Mon, Apr 20
Lectures
Mon, Apr 20Lecture 27: Special Topics: Generative Models for VisionModule: Learning Paradigms
Tue, Apr 21
Homeworks
Tue, Apr 21Homework 9: Learning Paradigms (Slot A)Module: Learning Paradigms
DUE
K-Means / Dimensionality Reduction: PCA
Wed, Apr 22
Quizzes
Wed, Apr 22Quiz 3 (HW7/HW8)Module: Learning Paradigms
Special Topics: Generative Models for Vision
Homeworks
Wed, Apr 22Homework 8: Reinforcement Learning (Slot B)Module: Reinforcement Learning
DUE
Reinforcement Learning: Policy Gradient / Deep RL
Sat, Apr 25
Homeworks
Sat, Apr 25Homework 9: Learning Paradigms (Slot B)Module: Learning Paradigms
DUE
K-Means / Dimensionality Reduction: PCA
16
Fri, May 1
Exams
Fri, May 1Exam 3Module: Learning Paradigms
EXAM
Special Topics: Generative Models for Vision