Is this what we'll learn to do in this class? Or is this what we'll learn not to do? 🤔🤔🤔
The chief objective of this course is to introduce standard statistical machine learning methods, including but not limited to various methods for supervised and unsupervised learning problems. Particular focus is on the conceptual understanding of these methods, their applications, and handson experience.Date  Topics  Lecture notes  Homework/ Practice problems 

08/25  Lecture: Introduction, Linear regression; Optimization algorithms Discussion: Linear algebra & Numpy review, Part I 
Lecture slides, Optimization Colab Discussion slides, Linear algebra Colab I 
Linear algebra questions I 
09/01  Lecture: Linear classifiers; Perceptron; Logistic regression Discussion: Probability review 
Lecture slides, Optimization Colab Discussion notes, Gaussians Python notebook 
Probability questions HW1, HW1 solutions 
09/08  Lecture: Generalization; Nonlinear basis; Regularization Discussion: Linear algebra & Numpy review, Part II 
Lecture slides, Nonlinear functions Colab Discussion notes, Linear Algebra Colab II 
Linear algebra questions II 
09/15  Lecture: L1 regularization; Kernel methods Discussion: HW1 review 
Lecture slides Discussion notes 
HW2, HW2 solutions 
09/22  Lecture: SVM Discussion: Problem discussion for Quiz 1 
Lecture slides, SVM Colab
Discussion notes 
Quiz 1 practice Quiz 1 practice (solutions) 
09/29  Lecture: Multiclass classification; Neural Networks Discussion: HW2 review  Lecture slides  
10/06  Lecture: Quiz 1 📝 No Discussion session  HW3, HW3 solutions  
10/13  Fall recess 🍁  
10/20  Lecture: Neural networks for images and sequences (and Markov models) Discussion: Quiz 1 review 
Lecture slides
Discussion notes 

10/27  Lecture: Decision trees; Ensemble methods Discussion: HW3 review 
Lecture slides Discussion notes 
HW4, HW4 solutions 
11/03  Lecture: Dimensionality reduction and visualization; PCA Discussion: Project overview 
Lecture slides Discussion notes 

11/10  Lecture: Clustering; kmeans; Gaussian mixture models; EM Discussion: Evaluation metrics (precision, recall etc.) 
Lecture slides, EM demo (exercise) Discussion notes 

11/17  Lecture: Density estimation; Generative models & Naive Bayes; Multiarmed bandits; Responsible ML Discussion: HW4 review 
Lecture slides Discussion notes 

11/24  Thanksgiving 🙏  
12/01  Lecture: Quiz 2 📝 No Discussion session  Practice problems, Practice problem solutions 

12/14  Project report due 📕  Project logistics 
Collaboration policy and academic integrity: Our goal is to maintain an optimal learning environment. You can discuss the homework problems at a high level with other groups, but you should not look at any other group's solutions. Trying to find solutions online or from any other sources for any homework or project is prohibited, will result in zero grade and will be reported. To prevent any future plagiarism, uploading any material from the course (your solutions, quizzes etc.) on the internet is prohibited, and any violations will also be reported. Please be considerate, and help us help everyone get the best out of this course.
Please remember the Student Conduct Code (Section 11.00 of the USC Student Guidebook). General principles of academic honesty include the concept of respect for the intellectual property of others, the expectation that individual work will be submitted unless otherwise allowed by an instructor, and the obligations both to protect one's own academic work from misuse by others as well as to avoid using another's work as one's own. All students are expected to understand and abide by these principles. Students will be referred to the Office of Student Judicial Affairs and Community Standards for further review, should there be any suspicion of academic dishonesty.
Students with disabilities: Any student requesting academic accommodations based on a disability is required to register with Disability Services and Programs (DSP) each semester. A letter of verification for approved accommodations can be obtained from DSP. Please be sure the letter is delivered to the instructor as early in the semester as possible.