Introduction to Machine Learning

• Command Line and File I/O Tutorial. 10601 Course Staff (2020).
• 10601 Learning Objectives. Matt Gormley (2023).
• Math Resources. 10601 Course Staff (2023).

HW1 Out

• 10601 Notation Crib Sheet. Matt Gormley (2023).

• Visual Information Theory. Christopher Olah (2015). blog.
• Decision Trees. Hal Daumé III (2017). CIML, Chapter 1.

HW1 Due

HW2 Out

• Geometry and Nearest Neighbors. Hal Daumé III (2017). CIML, Chapter 3.

• Limits of Learning. Hal Daumé III (2017). CIML, Chapter 2.

• The Perceptron. Hal Daumé III (2017). CIML, Chapter 4.
• Perceptron Demo. Geoff Gordon (2025).

• Linear Regression. Kevin P. Murphy (2014). Machine Learning: A Probabilistic Perspective. Chapter 7.1-7.3.

HW2 Due

HW3 Out

• An overview of gradient descent optimization algorithms. Sebastian Ruder (2017).

• Estimating Probabilities: MLE and MAP. Tom Mitchell (2016).
• Probability and Information Theory. Ian Goodfellow, Yoshua Bengio, & Aaron Courville (2016). Deep Learning, Chapter 3.
• Logistic Regression. Kevin P. Murphy (2014). Machine Learning: A Probabilistic Perspective. Chapter 1.4.6, 8.1-8.3, 8.6.

HW3 Due (only two grace/late days permitted)

• Regularization for Deep Learning. Ian Goodfellow, Yoshua Bengio, & Aaron Courville (2016). Deep Learning, Chapter 7.1 and 7.8.

• Deep Feedforward Networks. Ian Goodfellow and Yoshua Bengio and Aaron Courville (2016). Deep Learning, Chapter 6.1-6.4.

• Deep Feedforward Networks. Ian Goodfellow, Yoshua Bengio, & Aaron Courville (2016). Deep Learning, Chapter 6.5.
• Matrix Calculus for 10-301/601. Hoeseong (Hayden) Kim, Abhishek Vijayakumar (2022). 10601 Course Staff.

• Numpy Notes. 10601 Course Staff (2021).
• Logging Tutorial. 10601 Course Staff (2021).
• Workflow and Debugging Slides. 10601 Course Staff (2021).

HW4 Due

HW5 Out

• Generalization Abilities: Sample Complexity Results.. Nina Balcan (2015). Lecture notes.

• A Few Useful Things to Know about Machine Learning. Pedro Domingos (2012).

HW5 Due

HW6 Out

• Deep learning. Yann LeCun, Yoshua Bengio, & Geoffrey Hinton (2015). Nature.

Exam 2 Practice Problems out

• Deep learning. Yann LeCun, Yoshua Bengio, & Geoffrey Hinton (2015). Nature.

HW6 Due (only two grace/late days permitted)

• Deep learning. Yann LeCun, Yoshua Bengio, & Geoffrey Hinton (2015). Nature.

• Reinforcement Learning: A Survey. Kaelbling, et al (1996).

• Reinforcement Learning: A Survey. Kaelbling, et al (1996).

• Playing Atari with Deep Reinforcement Learning. Mnih, et al (2013).

HW7 Due

HW8 Out

• Matrix Factorization Techniques for Recommender Systems. Koren, Bell, and Volinsky (2009).

Exam 3 Practice Problems out

• Ensemble Methods. Hal Daumé III (2017). CIML, Chapter 13.
• A Brief Introduction to Boosting. Robert E. Schapire (1999).

• k-means++: The Advantages of Careful Seeding. Arthur & Vassilvitskii (2006).
• A Tutorial on Principal Component Analysis. Jonathon Shlens (2014).

HW8 Due

HW9 Out

• 10601 Learning Objectives. Matt Gormley (2023).

HW9 Due (only two grace/late days permitted)