Date	Topic	Reading	Items due
week 1 (Mon: 08/26)	Introduction to Machine Learning   Lecture Slides 1   Notebook#0 (released)
week 1 (Wed: 08/28)	Google Colab and Python Review   Day01 Notes: Python Refresher   Lecture Slides 2	Reading:	Notebook#0 (due on 08/28)
Week 2 (Wed: 09/02)	Labor Day (university holiday; classes do not meet)
week 2 (Wed: 09/04)	Pandas Tutorial   Day02 Notes: Pandas Tutorial   Lecture Slides 3	Reading:
week 3 (Mon: 09/09)	Practicing with Pandas (Give it a try first, and I'll provide a solution)   Day03 Notes: Pandas Practice   Lecture Slides 4   Notebook#1 (released on 09/08)	Reading:
week 3 (Wed: 09/11)	k-Nearest Neighbor (kNN)   Day04 Notes: k-Nearest Neighbor   Lecture Slides 5
week 4 (Mon: 09/16)	kNN code   Day05 Notes: kNN code   Lecture Slides 6   Notebook#2 (released on 09/16)	Reading:   The relationship ML and other fields   isna()   notna()   any()   value_counts()   dropna()   fillna()	Notebook#1 (due on 09/16)
week 4 (Wed: 09/18)	Missing Data and Normalization   Day06 Notes: Missing Data and Normalization   Lecture Slides 7	Reading:   sklearn: standard scalar
week 5 (Mon: 09/23)	Weighted k-NN   Day07 Notes: Normalization and Weighted k-NN   Lecture Slides 8   Quiz#1 (released on Blackboard 09/23)	Reading:   Pandas DataFrame: sample	Notebook#2 (due on 09/24)   Extended deadline
Week 5 (Wed: 09/25)	Graph Plot and Evaluation Metrics Discussion   Day08 Notes: Graph Plot   Lecture Slides 9	Reading:   matplotlib: Pyplot   sklearn.metrics: accuracy_score   sklearn.metrics: confusion_matrix   sklearn.metrics: mean_absolute_error   sklearn.metrics: mean_squared_error
week 6 (Mon: 09/30)	Evaluation Metrics on kNN Regression Coding Activity   Introduction to Decision Trees   Lecture Slides 10   Day09 Notes: Evaluation Metrics and Testing   Notebook#3 (released on 09/29)	Reading:	Quiz#1 (due on 09/30)
week 6 (Wed: 10/02)	Decision trees and Entropy Calculation   Paper-based in-class Activity on Decision Trees   Lecture Slides 11   Day10 Notes: Decision Trees	Reading:   sklearn: train and test split   sklearn: confusion matrix   sklearn: standard scalar
week 7 (Mon: 10/07)	Introduction to Scikit Learn   Lecture Slides 12a   Day11 Notes: introduction to sklearn	Reading:   sklearn: Decision Tree classifier   sklearn: k-Nearest-Neighbor Classifier   sklearn: evaluation metrics
week 7 (Wed: 10/09)	Scikit Learn Practice   Lecture Slides 12b   kNN, weighted-kNN, Decision Tree using Scikit Learn   Day12 Notes: Sklearn Practice   Notebook#4 (released on 10/09)	Reading:   sklearn: Decision Tree classifier   sklearn: k-Nearest-Neighbor Classifier	Notebook#3 (extended: due on 10/09)
week 8 (Mon: 10/14)	Fall Break (classes do not meet)
week 8 (Wed: 10/16)	Random Forest   Day13 Notes: Random Forest   Lecture Slides 13	Reading:   sklearn: Random Forest Classifier   Random Forests: Leo Breiman and Adele Cutler   Application of Random Forest: A Computer Vision research paper ICCV'2007   sklearn: Feature Selection: SelectKBest   sklearn: Principle Component Analysis (PCA)
week 9 (Mon: 10/21)	Day14 Notes: Dimensionality Reduction Techniques   Principle Component Analysis (PCA)   Lecture Slides 14   Project#1 (released on Blackboard 10/21)   Day14 Notes: Project#1	Reading:   sklearn: k-Nearest-Neighbor Classifier   sklearn: k-Nearest-Neighbor Regressor   sklearn: Decision Tree Classifier   sklearn: Decision Tree Regressor   sklearn: Random Forest Classifier   sklearn: Random Forest Regressor	Notebook#4 (due on 10/21)
week 9 (Wed: 10/23)	Linear Classifiers   Perceptron   Perceptron Update Algorithm   Lecture Slides 15	Reading:   The Perceptron: A Perceiving and Recognizing Automaton (Rosenblatt - 1957)
week 10 (Mon: 10/28)	Neuron Model and General Weight Learning Algorithm   Lecture Slides 16   In-class activity (linear model, perceptron)   Quiz#2 (released on Blackboard 10/28)   Day16 Notes: Perceptron Code (optional)	Reading:
week 10 (Wed: 10/30)	Virtual session (classes do not meet in-person)   Dr. Reza is traveling to China as part of a Drake University delegation;   Optimization: Gradient Descent, Stochastic Gradient Descent (SGD)   Lecture Slides 17   Day17 Notes: Loss Surface Visualization   Day17 Notes: Stochastic Gradient Descent (code)	Reading:
week 11 (Mon: 11/04)	Virtual session (classes do not meet in-person)   Dr. Reza is traveling to China as part of a Drake University delegation;	Reading:	Project #1 (due by 11/04)
week 11 (Wed: 11/06)	Introduction to Neural Networks   Multilayer Perceptron (MLP)   Lecture Slides 18	Reading:   Multilayer Perceptron (MLP)   The Backpropgation Algorithm (Rumelhart-1985)	Quiz#2 (due by 11/06)
Week 12 (Mon: 11/11)	PyTorch Basics   Lecture Slides 19   Day19 Notes: PyTorch Basics in-class activity   Day19 Notes: Building a very simple MLP using PyTorch	Reading:   PyTorch   PyTorch: matmul()   PyTorch: transpose()
Week 12 (Wed: 11/13)	Multilayer Perceptron (MLP) - (Modular PyTorch Implementation)   Lecture Slides 20   Day 20 Notes: PyTorch's Useful Functions for MLP   Day 20 Notes: Building Modular MLP with PyTorch	Reading:   PyTorch: nn.Linear()   PyTorch: nn.Sigmoid()   PyTorch: nn.ReLU()   PyTorch: nn.Softmax()   PyTorch: nn.Flatten()
Week 13 (Mon: 11/18)	Building and Training MLP using PyTorch   Lecture Slides 21a   Day 21 Notes: Training MLP with PyTorch   Convolutional Neural Network (CNN)   Lecture Slides 21b: Intro to CNN	Reading:   PyTorch: nn.CrossEntropyLoss()   PyTorch: nn.MSELoss()   PyTorch: optim.SGD()   PyTorch: optim.ADAM()   PyTorch: optim.RMSprop()   PyTorch: torchvision.datasets
Week 13 (Wed: 11/20)	Convolutional Neural Network (CNN)   Lecture Slides 22 [pdf] [powerpoint]   Day 22 Notes: Training CNN for image classification   Notebook #5 (Released)	Reading:   Earliest CNN: LeNet (LeCunn-1988)   PyTorch: nn.Conv2d()   PyTorch: nn.Flatten()   PyTorch: nn.MaxPool2d()   PyTorch: nn.AvgPool2d()
Week 14 (Mon: 11/25)	Fine-tuning vs. Training   Fine-tuning CNNs   Lecture Slides 23   Day 23 Notes	Reading:     Popular CNN: AlexNet (Krizevsky-2012)   Popular CNN: VGGNet (Simonyan-2015)   Popular CNN: ResNet (He-2015)
Week 14 (Wed: 11/27)	Thanksgiving break (classes do not meet)
Week 15 (Mon: 12/02)	Fine-tuning CNNs (continued)   Project #2 (released)   Recurrent Neural Network (RNN)   Lecture Slides 24a   Transformer   Lecture Slides 24b	Reading:     Transformer: Attention is All You Need (2017)   The Illustrated Transformer   Harvard University NLP: The Annotated   LSTM is dead. Long Live Transformers! - Paul Dirac talk   Transformer by Hand - Anna Rahn 5 min video tutorial	Notebook#5 (due on 12/06)   extended deadline
Week 15 (Wed: 12/04)	Large Language Model (LLM)   LLM Application for NLP Tasks using PyTorch   Lecture Slides 25   Day 25 Notes: LLM for sentiment classification   Day 25 Notes: LLM for paraphrase detection   Day 25 Notes: LLM for question answering     Quiz#3 (released on Blackboard 12/04)     Course Evaluation	Reading:   GPT-1 (100M parameters) - Radford et al. 2018 (OpenAI)   BERT (300M parameters) - Devlin et al. 2018   GPT-2 (1.5B parameters) - Radford et al. 2019 (OpenAI)   Megatron-LM (8.0B parameters) - Shoeybi et al. 2019 (NVidia)   T5 (11.0B parameters) - Raffel et al. 2020 (Google)   T-NLG (17.0B parameters) - Microsoft Corporation 2020 (Microsoft)   GPT-3 (175.0B parameters) - Brown et al. 2020 (OpenAI)   ChatGPT (1.5B parameters) - OpenAI 2022 (OpenAI)   Sparks of Artificial General Intelligence: Early experiments with GPT-4 - Microsoft Research '23   Dissociating language and thought in large language models - Mahowald, Tenenbaum et al.'24   OpenAI-o1 - 2024 (OpenAI)   Is OpenAI-o1 reasoning? (ML Street Talk'24)
Week 16 (Mon: 12/09)	No final exam!	Reading:	Quiz#3 (due on 12/13 Friday)   Project #2 (due by 12/14 Saturday)