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: |
Before the Lecture: During Lecture: |
Sep 18 |
Lecture 2 |
Supervised Learning Setup. Linear Regression. Basic probability theory review. Reading: |
Before the Lecture: During Lecture: |
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: |
Before the Lecture: During Lecture: |
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: |
Before the Lecture: During Lecture: |
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: |
Before the Lecture: During Lecture: |
Oct 18 |
Assignment 2 |
Assignment 2 will be posted online. |
|
Oct 23 |
Lecture 6 |
Neural Networks, forward and backpropagation. Improving NNs. Reading: |
Before the Lecture: During Lecture: |
Oct 30 |
Lecture 7 |
Deep learning, CNNs, RNNs, and beyond. Practical advice for your project. Reading: |
Before the Lecture: During Lecture: |
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: |
Before the Lecture: During Lecture: |
Nov 8 |
Assignment 3 |
Assignment 3 will be posted online. |
|
Nov 13 |
Lecture 9 |
Decision Trees. Boosting. Bagging. Principal and Independent Component Analysis. Reading: |
Before the Lecture: During Lecture: |
Nov 20 |
Lecture 10 |
Weak supervision. Critiques of Machine Learning. Reading: |
Before the Lecture: During Lecture: |
Nov 27 |
Lecture 11 |
Reinforcement learning. Value Iteration and Policy Iteration. Bias Variance trade-off. Reading: |
Before the Lecture: During Lecture: |
Dec 1 |
Assignment 3 |
Assignment 3 due at midnight (i.e., 11:59 PM or 23:59) eastern time. |
|
Dec 4 |
Lecture 12 |
Guest Lectures (see more details here). |
During Lecture: |
Dec 15 |
Final Project |
Final Project due at noon (i.e., 12 PM) eastern time. |