This table will be updated regularly throughout the Semester to reflect how to prepare for the next lecture and what is covered during each lecture along with corresponding readings and notes.

Date Event Description Materials and Instructions

Sep 11

Lecture 1

Introduction and Basic Concepts. Linear Algebra and Multivariable Calculus review.

Reading:
Notes on Linear Algebra.
Lecture 1 Slides.

Before the Lecture:
Recommended: Review background materials on Linear Algebra and Multivariable Calculus.

During Lecture:
Synchronous: Overview of machine learning and the course.
Asynchronous: Review of linear algebra and multivariable calculus.

Sep 18

Lecture 2

Supervised Learning Setup. Linear Regression. Basic probability theory review.

Reading:
Notes on Basic Probability
Sections 1-3 of the notes on Supervised Learning Setup & Linear Regression.

Before the Lecture:
Recommended: Review background materials on Basic Probability.

During Lecture:
Synchronous: Supervised Learning Setup. Linear Regression.
Asynchronous: Basic probability theory review.

Sep 20

Assignment 1

Assignment 1 will be posted online.

Sep 25

Lecture 3

Weighted Least Squares. Logistic Regression. Newton's Method. Exponential Family. Generalized Linear Models. Python overview.

Reading:
Sections 4-9 of the notes on Regression.

Before the Lecture:
Recommended: Review background materials on Python.

During Lecture:
Synchronous: Weighted Least Squares. Logistic Regression. Newton's Method.
Asynchronous: Exponential Family. Generalized Linear Models. Python overview.

Oct 2

Project Proposal

Project proposal due at noon (i.e., 12 PM) eastern time.

Oct 2

Lecture 4

Evaluation Metrics. Gaussian Discriminant Analysis. Naive Bayes.

Reading:
Sections 1-2 of the notes on Generative Models.

Before the Lecture:
Recommended: Review class notes.

During Lecture:
Synchronous: Gaussian Discriminant Analysis. Naive Bayes.
Asynchronous: Evaluation Metrics.

Oct 9

Assignment 1

Assignment 1 due at noon (i.e., 12 PM) eastern time.

Oct 9

Lecture 5

Laplace Smoothing. Support Vector Machines.

Reading:
Sections 2 of the notes on Generative Models.
Sections 1-5, 7 of the notes on Kernels and SVM.

Before the Lecture:
Recommended: Review class notes.

During Lecture:
Synchronous: Laplace Smoothing. Support Vector Machines.
Asynchronous: Support Vector Machines.

Oct 18

Assignment 2

Assignment 2 will be posted online.

Oct 23

Lecture 6

Neural Networks, forward and backpropagation. Improving NNs.

Reading:
Notes on Deep Learning and Backpropagation.

Before the Lecture:
Recommended: Review class notes.

During Lecture:
Synchronous: Neural Networks, forward and backpropagation.
Asynchronous: Improving NNs.

Oct 30

Lecture 7

Deep learning, CNNs, RNNs, and beyond. Practical advice for your project.

Reading:
Lecture notes and slides will be posted after the lecture.

Before the Lecture:
Recommended: Have a very good sense of your progress in the project and challenges you are currently facing. You should be about halfway through your project.

During Lecture:
Synchronous: Practical advice for your project.
Asynchronous: Deep learning, CNNs, RNNs, and beyond.

Nov 6

Assignment 2

Assignment 2 due at noon (i.e., 12 PM) eastern time.

Nov 6

Lecture 8

Regularization. Feature/Model selection. K-Means. GMM.

Reading:
Notes on Regularization. Feature/Model selection.
Notes on k-means.
Notes on GMM.

Before the Lecture:
Recommended: Review class notes.

During Lecture:
Synchronous: K-Means. GMM.
Asynchronous: Regularization. Feature/Model selection.

Nov 8

Assignment 3

Assignment 3 will be posted online.

Nov 13

Lecture 9

Decision Trees. Boosting. Bagging. Principal and Independent Component Analysis.

Reading:
Notes on Principal Component Analysis.
Notes on Independent Component Analysis.

Before the Lecture:
Recommended: Review class notes.

During Lecture:
Synchronous: Principal and Independent Component Analysis.
Asynchronous: Decision Trees. Boosting. Bagging.

Nov 20

Lecture 10

Weak supervision. Critiques of Machine Learning.

Reading:
Notes on Weak Supervision.

Before the Lecture:
Recommended: Review class notes.

During Lecture:
Synchronous: Weak supervision.
Asynchronous: Critiques of Machine Learning.

Nov 27

Assignment 3

Assignment 3 due at noon (i.e., 12 PM) eastern time.

Nov 27

Lecture 11

Reinforcement learning. Value Iteration and Policy Iteration. Bias Variance trade-off.

Reading:
Notes on Reinforcement learning.
Notes on Bias Variance trade-off.

Before the Lecture:
Recommended: Review class notes.

During Lecture:
Synchronous: Reinforcement learning. Value Iteration and Policy Iteration.
Asynchronous: Bias Variance trade-off.

Dec 4

Lecture 12

Advanced topics. Guest Lectures. Course wrap up.

Reading:
Lecture notes and slides will be posted after the lecture.

During Lecture:
Synchronous: Guest lectures. Course wrap up.
Asynchronous: Advanced topics.

Dec 15

Final Project

Final Project due at noon (i.e., 12 PM) eastern time.