Time and location: Tue, Thu 5:30 – 6:45pm, Woodward 130

Instructor & TAs:		Razvan Bunescu		Alec Pike
Office:		Woodward 410G		Cone 164
Office hours:		Tue, Thu 2:30 – 3:30pm		Mon, Wed 1:00 – 2:00pm
Email:		rbunescu @ charlotte edu		apike5 @ charlotte edu

• Pattern Recognition and Machine Learning by Christopher Bishop. Springer, 2007.
• Deep Learning by Christopher Bishop and Hugh Bishop, Springer, 2024.
• Dive into Deep Learning by Zhang, Lipton, Li, and Smola. Amazon, 2019.
• Mathematical Pathways to Machine Learning by Xiantao Li. Pennsylvania State University, 2026.

1. Syllabus & Introduction with the simple Perceptron
   • Notes from lecture on Aug 20.
   • Notes from lecture on Aug 25: one, two, and three.
   • Notes from lecture on Aug 27: one and two.
2. Programming with Python
   • Python examples: PDF and notebook.
   • Python tutorial
   • Python for Everybody
3. Basic linear algebra
   • Chapter 2 in DL textbook on Linear Algebra.
4. NumPy for linear algebra
   • Tutorials on NumPy and SciPy.
     • Broadcasting explained.
5. k-Nearest Neighbors for classification and regression
6. Differentiation and optimization
   • Symbolic differentiation with SymPy.
   • Automatic differentiation with Autograd.
7. Linear regression and ordinary least squares
8. Gradient descent and least mean squares
   • An overview of gradient descent optimization algorithms, Sebastian Ruder, CoRR 2016
9. Curve fitting and regularization
10. Kernel Perceptron and Averaged Perceptron
    • LibSVM applet demonstrating kernels.
11. Probability theory
12. Maximum Likelihood Estimation principle
    • Sections 2.1 and 2.2 in Tom Mitchell's chapter on Estimating Probabilities
13. Naive Bayes
    • Chapter 4 from Speech and Language Processing, by Jurafsky and Martin, 2024.
14. Logistic regression
15. Computation graphs and automatic differentiation in PyTorch
    • PyTorch tutorials.
16. Multilayer Perceptrons, Backpropagation, and Deep Learning
    • Chapter 6 on Neural Networks from the NLP textbook.
    • Andrej Karpathy: "Yes you should understand backprop"
17. Reinforcement learning

• Assignment 0 on basic programming in Python
  • Skeleton code and data
• Assignment 1 on Perceptrons using NumPy
  • Skeleton code and data
• Assignment 2 on kNNs using NumPy
  • Skeleton code and data
• Assignment 3 on Linear regression (normal equations)
  • Skeleton code and data
• Assignment 4 on Linear regression (gradient descent)
  • Skeleton code and data
• Assignment 5 on Polynomial Curve Fitting
  • Skeleton code and data
• Assignment 6 on Kernel Perceptron and Average Perceptron.
  • Skeleton code and data
• Assignment 7 on Probability Theory and Naive Bayes
  • Skeleton code and data
• Assignment 8 on Logistic Regression in NumPy and PyTorch.
  • Skeleton code and data
• Assignment 9 on Neural Networks in NumPy and PyTorch.
  • Theory
  • Implementation: (1) NNs in Numpy and (2) NNs in Pytorch.
    • Skeleton code and data

• Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Cambridge University Press, 2020.
• A Course in Machine Learning book by Hal Daume III
• Machine Learning book by Tom Mitchell. McGraw Hill, 1997
• Supervised Machine Learning video lectures @ Coursera.
• Mathematics for Deep Learning and Data Science video lectures @ Coursera:
  • Linear Algebra module.
  • Calculus module.
  • Probability and Statistics module.
• Machine Learning Crash Course from Google.

• Python programming:
  • Google's Crash Course on Python, free for UNCC students
  • W3 Schools' Python tutorial
  • Microsoft's Python for beginners
  • Python for Everybody
  • Python lecture
• Probability and statistics:
  • Basic probability theory (pp. 12-19) in Pattern Recognition and Machine Learning.
  • Chapter 3 in DL textbook on Probability and Information Theory.
  • Parts 1 and 2 in CS109: Probability for Computer Scientists course reader.
  • Seeing Theory: A visual introduction to probability and statistics, Kunin et al., 2018.
• Linear Algebra:
  • Chapter 2 in DL textbook on Linear Algebra.
  • Interactive Linear Algebra, Margalit and Rabinoff, Georgia Tech, 2017.
  • Chapter 2 on Linear Algebra in Mathematics for Machine Learning.
  • Inderjit Dhillon's Linear Algebra Background
  • Gilbert Strang's Introduction to Linear Algebra
  • Petersen et al.'s The Matrix Cookbook
  • Mike Brookes' Matrix Reference Manual
• Calculus:
  • Basic properties for derivatives, integrals, exponentials, and logarithms.
  • Chapter 4.3 in DL textbook on Numerical Computation.
  • Gilbert Strang's Calculus texbook.

• PyTorch Deep learning in Python
• PyTorch tutorial from University of Amsterdam.
• Scikit-learn Machine learning in Python
• Hugging Face Transformers