Date	Topic	Reading	Items due
week 1 (Mon: 01/26)	Introduction to Machine Learning   Lecture Slides 1   Notebook#0 (released)
week 1 (Wed: 01/28)	Google Colab and Python Review   Day02 Notes: Python Refresher   Lecture Slides 2	Reading:	Notebook#0 (due on 01/30)
week 2 (Mon: 02/02)	Pandas Tutorial   Day03 Notes: Pandas Tutorial   Lecture Slides 3	Reading:
week 2 (Wed: 02/04)	Practicing with Pandas (Give it a try first, and I'll provide a solution)   Day04 Notes: Pandas Practice   Lecture Slides 4   Notebook#1 (released on 02/04)	Reading:
week 3 (Mon: 02/09)	Missing Data   Day05 Notes: Handling Missing Data   Lecture Slides 5	Reading:   isna()   notna()   any()   value_counts()   dropna()   fillna()	Notebook#1 (due on 02/11)
week 3 (Wed: 02/11)	k-Nearest Neighbor (kNN)   Day06 Notes: k-Nearest Neighbor using Pandas Library   Lecture Slides 6	Reading:   The relationship ML and other fields
week 4 (Mon: 02/16)	Introduction to Scikit-learn Library   kNN code using Scikit-learn Library (more efficient)   Day07 Notes: kNN code with scikit-learn   Lecture Slides 7   Notebook#2 (released on 02/16)	Reading:   sklearn   sklearn::KNeighborsClassifier   sklearn::KNeighborsRegressor
week 4 (Wed: 02/18)	Data Normalization   Day08 Notes: Data Normalization   Lecture Slides 8	Reading:   sklearn: standard scalar	Notebook#2 (due on 02/23)
Week 5 (Mon: 02/23)	Weighted k-NN   Graph Plot   Cross-validation Discussion   Day09 Notes: Weighted k-NN code using sklearn   Day09 Notes: Graph Plot (take home activity due by 02/25)   Lecture Slides 9	Reading:   matplotlib: Pyplot
week 5 (Wed: 02/25)	Evaluation Metrics (Accuracy, Confusion Matrix, MAE, MSE, R2)   Evaluation Metrics on kNN Regression Coding Activity   Day10 Notes: Evaluation Metrics and Testing   Lecture Slides 10   Notebook#3 (released on 02/25)	Reading:   sklearn.metrics: accuracy_score   sklearn.metrics: confusion_matrix   sklearn.metrics: mean_absolute_error   sklearn.metrics: mean_squared_error
week 6 (Mon: 03/02)	Introduction to Decision Trees   Decision trees and Entropy Calculation   Paper-based in-class Activity on Decision Trees   Day10 Notes: Entropy Calculation for Decision Trees   Lecture Slides 11	Reading:
week 6 (Wed: 03/04)	Decision Tree code using Scikit-learn Library   Day12 Notes: Decision tree code using Scikit-learn   Day12 Notes: More practice exercises using Scikit-learn   Lecture Slides 12	Reading:   sklearn: Decision Tree classifier   sklearn: train and test split   sklearn: evaluation metrics   sklearn: confusion matrix	Notebook#3 (due on 03/04)
week 7 (Wed: 03/09)	Random Forest   Day13 Notes: Random Forest   Lecture Slides 13   Notebook#4 (released on 03/06)	Reading:   sklearn: Random Forest Classifier   sklearn: Random Forest Regressor   Random Forests: Leo Breiman and Adele Cutler   Application of Random Forest: A Computer Vision paper
week 7 (Wed: 03/11)	Content Quiz#1 (in-class exam released on Blackboard)		Content Quiz#1 (due on 03/11)
week 8 (Mon: 03/16)	Spring Break (classes do not meet)		Notebook#4 (due on 03/13)
week 8 (Mon: 03/18)	Spring Break (classes do not meet)
week 9 (Mon: 03/23)	Day14 Notes: Dimensionality Reduction Techniques   Principle Component Analysis (PCA)   Lecture Slides 14   Project#1 (released on Blackboard 03/23)   Day14 Notes: Project#1	Reading:   sklearn: Principle Component Analysis (PCA)   sklearn: Feature Selection: SelectKBest
week 9 (Wed: 03/25)	Linear Classifiers   Perceptron   Lecture Slides 15	Reading:
week 10 (Mon: 03/30)	Perceptron (continued)   Perceptron Learning Algorithm   Lecture Slides 16   In-class activity (linear model, perceptron)   Day16 Notes: Perceptron Code (optional)	Reading:   The Perceptron: A Perceiving and Recognizing Automaton (Rosenblatt - 1957)
week 10 (Wed: 04/01)	Neuron Model and General Weight Learning Algorithm   Optimization: Gradient Descent (GD), Stochastic Gradient Descent (SGD)   Lecture Slides 17   Day17 Notes: Loss Surface Visualization   Day17 Notes: Stochastic Gradient Descent (code)	Reading:   An overview of gradient descent optimization algorithms - Sebastian Ruder
Week 11 (Mon: 04/06)	Introduction to Neural Networks   Multilayer Perceptron (MLP)   Lecture Slides 18	Reading:   Multilayer Perceptron (MLP)   The Backpropgation Algorithm (Rumelhart-1985)	Project#1 (due by 04/06)
Week 11 (Mon: 04/08)	PyTorch Basics   Lecture Slides 19   Day20 Notes: PyTorch Basics in-class activity   Day20 Notes: Building a very simple MLP using PyTorch	Reading:   PyTorch   PyTorch: matmul()   PyTorch: transpose()   PyTorch: nn.Linear()   PyTorch: nn.Sigmoid()   PyTorch: nn.ReLU()