Basic Info

Instructor: Dr. Weitian Tong     Associate Professor at Department of Computer Science
Office: IT 2305
Email: wtong@georgiasouthern.edu

Please refer to our syllabus on Folio for more details, such as prerequisites, textbook, topics, objectives, learning outcomes, assessment plan, course policies, etc.

Course Schedule (timely updated!)

Date	Topics	Resources
Week 1: 01/13 – 01/19	Class begins on 01/13! Class 1: Attendance Verification Syllabus Prerequisites and prework Project: Zero to Mastery Machine Learning Managing ML projects Class 2: Introduction to Machine Learning Part 1 Part 2 History of machine learning	Tutorials: Google Machine Learning Education Machine Learning, Animated Machine Learning for Beginners - A Curriculum Cheat Sheet: Machine Learning Glossary Super VIP Cheatsheet: Machine Learning Useful Collections: Start Machine Learning in 2024-Become an expert for free Awesome Machine Learning and AI Courses YouTube: Machine Learning: StatQuest with Josh Starmer Machine Learning (1) - Basic Models Machine Learning (2) - Ensemble models Deep Learning (1) - Artificial Neural Network Deep Learning (2) - Deep Neural Networks Machine Learning for Everybody – Full Course ML YouTube Courses Scikit-Learn Tutorials - Master Machine Learning All Machine Learning algorithms explained in 17 min Other: Papers with Code: a free and open resource with Machine Learning papers, code, datasets, methods and evaluation tables
Week 2: 01/20 – 01/26	Class 1: Introduction to Python and Anaconda Google's Python Class Class 2: Introduction to Python and Anaconda (continue) Google's Python Class Introduction to Numpy; A Visual Intro to NumPy and Data Representation	Python Tutorial Free Interactive Python Tutorial CONDA Cheat Sheet Markdown Cheat Sheet Videos: Anaconda (Conda) for Python - What & Why? Markdown Crash Course Jupyter Notebook Complete Beginner Guide 2023 Windows Command Line Crash Course Linux Command Line Crash Course Python for Beginners - Learn Python in 1 Hour Python NumPy Tutorial for Beginners Complete Python Pandas Data Science Tutorial! (2024 Updated Edition) Intro to Data Visualization in Python with Matplotlib
Assignment 1 (Introduction to Machine Learning)
Week 3: 01/27 – 02/02	Class 1: Introduction to Pandas Introduction to Matplotlib (Optional) Class 2: Linear Regression	Stochastic gradient descent Gradient Descent with Animation Animation meets Gradient Decent Interactive Gradient Descent Demo Gradient Descent Visualization Videos: Linear Regression Gradient Descent, Step-by-Step Stochastic Gradient Descent Intro to Gradient Descent
Week 4: 02/03 – 02/09	Class 1: Linear Regression (Continue) Class 2: Logistic Regression	Intuition behind Log-Loss Score Videos StatQuest: Logistic Regression Logistic Regression Details Pt1: Coefficients Logistic Regression Details Pt 2: Maximum Likelihood Logistic Regression Details Pt 3: R-squared and p-value
Assignment 2 (Linear regression)
Week 5: 02/10 – 02/16	Class 1: Classification Class 2: Classification (Continue)	A Visual Explanation of Receiver Operating Characteristic Curves and Area Under the Curve Confusion Matrix Cacluator Confusion Matrix Cacluator Videos Machine Learning in Python: Building a Classification Model Machine Learning Fundamentals: The Confusion Matrix ROC and AUC, Clearly Explained
Assignment 3 (Logistic regression) Assignment 4 (Classification)
Week 6: 02/17 – 02/23	Class 1: Datasets, generalization, and overfitting Class 2: Datasets, generalization, and overfitting (Continue) Working with numerical data	Videos: Normalization Vs. Standardization Train Test Split Python Feature Scaling Handling Missing Data in Python Simplify Data Preprocessing with Python's Column Transformer Data Preprocessing in Machine learning Data Cleaning in Pandas
Assignment 5 (Datasets)
Week 7: 02/24 – 03/02	Class 1: Working with numerical data (Continue) Class 2: Working with categorical data One Hot Encoder with Python Ordinal Encoder with Python	Videos: One Hot Encoder with Python Ordinal Encoder with Python Bagging vs Boosting - Ensemble Learning In Machine Learning Explained
Assignment 6 (Data Preprocessing)
Week 8: 03/03 – 03/09	Class 1: Machine Learning Problem Framing Explore Machine Learning: Problem Framing Class 2: Decision Tree Entropy (for data science) Decision Tree Classification Decision Tree Classification in Python (from scratch!)	Information Gain; or Information Gain A visual introduction to machine learning Model Tuning and the Bias-Variance Tradeoff Videos: Explore Machine Learning: Problem Framing Decision and Classification Trees, Clearly Explained!!! Decision Trees, Part 2 - Feature Selection and Missing Data Regression Trees, Clearly Explained!!! StatQuest: Random Forests Part 1 - Building, Using and Evaluating StatQuest: Random Forests Part 2: Missing data and clustering Random Forest Algorithm Clearly Explained! Random Forest Algorithm Explained with Python and scikit-learn
Please provide your rough ideas about your Project!
Assignment 7 (Machine Learning Problem Framing)
Week 9: 03/10 – 03/16	Class 1: Decision Forests Random Forest Algorithm Visual Guide to Gradient Boosted Trees Class 2: Clustering K-means Clustering From Scratch In Python	Videos: StatQuest: K-means clustering Clustering with DBSCAN, Clearly Explained!!! StatQuest: Hierarchical Clustering
Assignment 8 (Decision Tree and Forest)
Week 10: 03/17 – 03/23	Spring break for students – No classes
Week 11: 03/24 – 03/30	Class 1: Neural networks Class 2: No Zoom lecture. This class is for students to work on the coding competition and your course project.	A Visual And Interactive Look at Basic Neural Network Math Videos: What is a Neural Network? | DL1 Gradient descent, how neural networks learn | DL2 Backpropagation, step-by-step | DL3 Backpropagation calculus | DL4 Transformers (how LLMs work) explained visually | DL5 Attention in transformers, visually explained | DL6 How might LLMs store facts | DL7 A Beginner's Guide to Vector Embeddings Word Embedding and Word2Vec, Clearly Explained!!! Intro to Large Language Models State of GPT Let's build the GPT Tokenizer Let's build GPT: from scratch, in code, spelled out. The Exciting, Perilous Journey Toward AGI | Ilya Sutskever | TED The Inside Story of ChatGPT’s Astonishing Potential | Greg Brockman | TED "Godfather of AI" Geoffrey Hinton: The 60 Minutes Interview Andrew Ng: Opportunities in AI - 2023 The Near Future of AI [Entire Talk] - Andrew Ng (AI Fund)
Assignment 9 (Clustering) Assignment 10 (Neural networks)
Week 12: 03/31 – 04/06	Class 1: Embedding A Beginner's Guide to Vector Embeddings Class 2: Image Classification Convolutional Neural Networks cheatsheet Convolutional Neural Networks | Deep Learning Animated Visualizing Convolutional Neural Networks | Layer by Layer Convolutional Neural Networks from Scratch | In Depth PyTorch Tutorial 14 - Convolutional Neural Network (CNN)	Videos: Machine Learning Engineering for Production A Brief Introduction to Automated Machine Learning (AutoML) Definitions of Fairness in Machine Learning
Week 13: 04/07 – 04/13	Class 1: Recurrent Neural Networks Recurrent Neural Networks cheatsheet LSTM Recurrent Neural Networks (RNNs), Clearly Explained!!! Long Short-Term Memory (LSTM), Clearly Explained Long Short-Term Memory with PyTorch + Lightning Class 2: Generative Adversarial Networks Transformer The Annotated Transformer Transformers (how LLMs work) explained visually | DL5 Attention in transformers, visually explained | DL6	Videos: Convolutional Neural Networks | Deep Learning Animated PyTorch Tutorial 14 - Convolutional Neural Network (CNN) Convolutional Neural Network from Scratch | Mathematics & Python Code Visualizing Convolutional Neural Networks | Layer by Layer Convolutional Neural Networks from Scratch | In Depth Training a Convolutional Neural Network (CNN) Backpropagation in Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs), Clearly Explained!!! Long Short-Term Memory (LSTM), Clearly Explained Long Short-Term Memory with PyTorch + Lightning
Week 14: 04/14 – 04/20	Class 1: Large language models Intro to Large Language Models Class 2: Presentations for competitions and projects (TBD)	The Annotated Transformer Videos: A Friendly Introduction to Generative Adversarial Networks (GANs) Attention for Neural Networks Transformer Neural Networks, ChatGPT's foundation Decoder-Only Transformers, ChatGPTs specific Transformer Encoder-Only Transformers (like BERT) Coding a ChatGPT Like Transformer From Scratch in PyTorch
Week 15: 04/21 – 04/27	Presentations for competitions and projects
Week 16: 04/28 – 05/04	Presentations for competitions and projects
All Coding Competitions. Deadline: 11:59 pm, 05/04/2025 Titanic - Machine Learning from Disaster House Prices - Advanced Regression Techniques Digit Recognizer Natural Language Processing with Disaster Tweets
Week 17: 05/05 – 05/11	The last day of classes is 05/05. We actually have no classes this week.
Project. Deadline: 11:59 pm, 05/05/2025