### Tentative schedule for COMP 451

Lec. | Date | Topic | Reading | Additional Info |
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Introduction to machine learning |
Lecture notes (Chap 1) Jamboard Mandatory reading: This paper. | |||

## Part I: Decision Boundaries |
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Instance-based learning |
Lecture notes (Chap 2) Jamboard |
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Parametric Learning and Perceptrons (Part I) |
Lecture notes (Chap 3; Sec 3.1-3.2) Jamboard |
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Parametric Learning and Perceptrons (Part II) |
Lecture notes (Chap 3; Sec 3.3) Jamboard |
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## Part II: Likelihood |
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Maximum Likelihood |
Lecture notes (Chap 4) Jamboard |
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Naive Bayes (Part I) |
Lecture notes (Chap 5; Sec 5.1-5.3) Jamboard |
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Naive Bayes (Part II) |
Lecture notes (Chap 5; Sec 5.3-5.5) Jamboard |
Theory Assignment 1 Released Practice Assignment 1 Practice Assignment 1 (with solutions) Assignment 1 [Theory Assignment 1] (due Feb 9th) |
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Logistic Regression |
Lecture notes (Chapter 6) Jamboard |
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## Part III: Optimization |
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Empirical Risk Minimization |
Lecture notes (Chapter 7) Jamboard |
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Gradient Descent and Convexity |
Lecture notes (Chapter 8) Jamboard |
Theory Assignment 1 Due | ||

Linear Regression |
Lecture notes (Chapter 9) Jamboard |
Assignment 2 [Practical Assignment 1] (due Feb 23rd) |
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Generalization and Overfitting |
Lecture notes (Chapter 10) Jamboard |
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Regularization |
Lecture notes (Chapter 11) Jamboard |
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## Part IV: Information |
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Information Theory |
Lecture notes (Chapter 12) Jamboard |
Assignment 2 / Practical Assignment 1 Due Theory Assignment 2 (Practice) Theory Assignment 2 (Practice with Solutions) Assignment 3 [Theory Assignment 2] (due March 11) |
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Decision Trees |
Lecture notes (Chapter 13) Jamboard | |||

Study Break |
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Study Break |
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## Part V: Latent Variables |
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Clustering |
Lecture notes (Chapter 14) Jamboard | |||

Mixture Models |
Lecture notes (Chapter 15) Jamboard |
Assignment 3 [Theory Assignment 2] Due Assignment 4 [Practical Assignment 2] (due March 23rd) |
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## Part VI: Representations |
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Feature Design |
Lecture notes (Chapter 16) Jamboard |
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PCA |
Lecture notes (Chapter 17) Jamboard |
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Boltzmann Machines |
Lecture notes (Chapter 18) Jamboard |
Practical Assignment 2 Due | ||

## Part VII: Neural Networks |
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Feedforward Neural Networks |
Slides |
Theory Assignment 3 (Practice) Theory Assignment 3 (Practice with Solutions) Assignment 5 [Theory Assignment 3] (due April 6) |
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Optimizing Neural Networks |
Slides |
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Convolutional Neural Networks |
Slides |
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Recurrent Neural Networks |
Slides |
Theory Assignment 3 Due | ||

Autoencoders |
Slides |
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Review |