Date	Topic	Reading	Items due
week 1 (Tue: 08/29)	Introduction to Machine Learning   Day00 Notes: Introduction   Lecture Slides 1   N0 (released)
week 1 (Thu: 08/31)	Google Colab and Python Review   Day01 Notes: Python Refresher   Lecture Slides 2	Reading:	N0 (due on 08/31)
week 2 (Tue: 09/05)	Pandas Tutorial   Day02 Notes: Pandas Tutorial   Lecture Slides 3   Notebook#1 (released)	Reading:
week 2 (Thu: 09/07)	Practicing with Pandas   Day03 Notes: Pandas Practice   Lecture Slides 4	Reading:
week 3 (Tue: 09/12)	k-Nearest Neighbor (kNN)   Day04 Notes: k-Nearest Neighbor   Lecture Slides 5   Notebook#2 (released)		Notebook#1 (due on 09/12)
week 3 (Thu: 09/14)	kNN code, Missing Data and Normalization   Day05 Notes: kNN code, Missing Data and Normalization   Lecture Slides 6	Reading:   The relationship ML and other fields   isna()   notna()   any()   value_counts()   dropna()   fillna()
week 4 (Tue: 09/19)	Normalization and Weighted k Nearest Neighbor (kNN)   Day06 Notes: Normalization and Weighted k-Nearest Neighbor   Lecture Slides 7   Q1 (released on Blackboard)	Reading:   sklearn: standard scalar	Notebook#2 (due on 09/20)
week 4 (Thu: 09/21)	Weighted k-NN, Graph Plot, and Metrics   Day07 Notes: Weighted k-NN, Graph, and Metrics   Lecture Slides 8   Notebook#3 (released)	Reading:   Pandas DataFrame: sample   matplotlib: Pyplot
week 5 (Tue: 09/26)	Evaluation Metrics and Introduction to Decision Trees   Day08 Notes: Metrics and Testing   Lecture Slides 9	Reading:   sklearn.metrics: accuracy_score   sklearn.metrics: confusion_matrix   sklearn.metrics: mean_absolute_error   sklearn.metrics: mean_squared_error	Q1 (due on 09/26)
week 5 (Thu: 09/28)	Decision Trees   Day09 Notes: Decision Trees   Lecture Slides 10	Reading:
week 6 (Tue: 10/03)	Introduction to Scikit Learn   Watch the video lectures on Blackboard   Day10 Notes: introduction to sklearn   Notebook#4 (released)	Reading:   sklearn: train and test split   sklearn: Decision Tree classifier   sklearn: k-Nearest-Neighbor Classifier   sklearn: evaluation metrics   sklearn: confusion matrix   sklearn: standard scalar	Notebook#3 (due on 10/03)
week 6 (Thu: 10/05)	Scikit Learn Practice   Day11 Notes: sklearn practice	Reading:
week 7 (Tue: 10/10)	Random Forests   Day12 Notes: Random Forests   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	Notebook#4 (due on 10/12)
week 7 (Thu: 10/12)	Project #1   Day13 Notes: Project #1   Project #1 (released)	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
week 8 (Tue: 10/17)	Fall Break (no class)
week 8 (Thu: 10/19)	Project # 1 (discussion)   Dimensionality Reduction (feature selection and feature extraction)   Day13 Notes: Dimensionality Reduction   Lecture Slides 15	sklearn: Feature Selection: SelectKBest   sklearn: Principle Component Analysis (PCA)
week 9 (Tue: 10/24)	Principle Component Analysis (PCA)   Support Vector Machine (SVM)   Lecture Slides 16	Reading:
week 9 (Thu: 10/26)	Support Vector Machine (SVM) (continued)   Lecture Slides 17   In-class activity (vector-vector dot product)   Q2 (released)	Reading:	Project #1 (due by 10/26)
week 10 (Tue: 10/31)	Day16 Notes: SVM code   Introduction to Neural Networks -- Perceptrons   Lecture Slides 18   In-class activity (linear model, perceptron)   Notebook #5	Reading:
week 10 (Thu: 11/02)	Perceptron Update Rule   Day18 Notes: Perceptron Code   Lecture Slides 19	Reading:   The Perceptron: A Perceiving and Recognizing Automaton (Rosenblatt - 1957)	Q2 (due on 11/03)
week 11 (Tue: 11/07)	Optimization: Gradient Descent, Stochastic Gradient Descent (SGD)   Lecture Slides 20   Day19 Notes: Loss Surface Visualization + Gradient Descent   Day19 Notes: Stochastic Gradient Descent (code)	Reading:	Notebook #5 (due on 11/10)
week 11 (Thu: 11/09)	Multilayer Perceptron (MLP)   PyTorch Basics   Lecture Slides 21   PyTorch Basics in-class activity	Reading:   Multilayer Perceptron (MLP)   The Backpropgation Algorithm (Rumelhart-1985)   PyTorch   PyTorch: matmul()   PyTorch: transpose()
week 12 (Tue: 11/14)	Multilayer Perceptron (MLP) - (PyTorch Implementation)   Day 22 Notes	Reading:   PyTorch: nn.Linear()   PyTorch: nn.Sigmoid()   PyTorch: nn.ReLU()   PyTorch: nn.Softmax()   PyTorch: nn.Flatten()   PyTorch: nn.CrossEntropyLoss()   PyTorch: nn.MSELoss()   PyTorch: optim.SGD()   PyTorch: optim.ADAM()   PyTorch: optim.RMSprop()   PyTorch: torchvision.datasets
week 12 (Thu: 11/16)	Convolutional Neural Networks (CNN)   Lecture Slides 23	Reading:   Popular CNN: LeNet (LeCunn-1988)   Popular CNN: AlexNet (Krizevsky-2012)   Popular CNN: VGGNet (Simonyan-2015)   Popular CNN: ResNet (He-2015)
week 13 (Tue: 11/21)	Convolutional Neural Network (CNN) - (PyTorch Implementation)   Day 24 Notes   Notebook #6	Reading:   PyTorch: nn.Conv2d()   PyTorch: nn.Flatten()   PyTorch: nn.MaxPool2d()   PyTorch: nn.AvgPool2d()
week 13 (Thu: 11/23)	Thanksgiving break! (no class)	Reading:
week 14 (Tue: 11/28)	Recurrent Neural Network (RNN)   RNN for Natural Language Processing (NLP)   Lecture Slides 25   Project #2	Reading:   RNN: Serial Order (Jordan-1986)   Finding Structure in Time (Elman-1990)   LSTM: Long Short-Term Memory (Hochreiter-1997)   GRU: Gated Recurrent Unit (Hochreiter-2014)   The Unreasonable Effectiveness of Recurrent Neural Networks (RNNs)
week 14 (Thu: 11/30)	Transformer   Lecture Slides 26	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	Notebook #6 (due on 11/29)
week 15 (Tue: 12/05)	Large Language Model (LLM)   Lecture Slides 27   Project #2 work time   Q3	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)
week 15 (Thu: 12/07)	Vision and Language Model (CLIP, BLIP)   Lecture Slides 28   Course evaluation	Reading:   Interacting with CLIP
Week 16 (Tue: 12/12)	No final exam!	Reading:	Q3 (due on 12/12)   Project #2 (due by 12/12)