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 

1/12  Lecture 1: Introduction, Linear regression; Optimization algorithms Discussion: Linear algebra review 
Lecture slides, Optimization Colab Discussion slides 
Linear algebra questions I 
1/19  Lecture 2: Linear classifiers; Perceptron; Logistic regression Discussion: Probability review 
Lecture slides, Optimization Colab Discussion slides 
Probability questions HW1, HW1 solutions 
1/26  Lecture 3: Generalization; Nonlinear basis; Regularization Discussion: Linear algebra review 
Lecture slides, Nonlinear functions Colab Discussion notes 
Linear algebra questions II 
2/2  Lecture 4: L1 regularization; Kernel methods Discussion: Linear algebra & Numpy review 
Lecture slides Discussion notes 

2/9  Lecture 5: SVM Discussion: HW1 review  Lecture slides, SVM Colab  HW2, HW2 solutions 
2/16  Lecture 6: Multiclass classification; Neural Networks Discussion: Problem discussion for Exam 1  Lecture slides 
Practice problems, Practice problems (solution) 
2/23  Lecture 7: Optimization for neural networks, CNNs Discussion: HW2 review  Lecture slides, Discussion notes  
3/1  Lecture: Exam 1 📝 No Discussion session  
3/8  Lecture 8: Language modelling, Markov models, RNNs, Attention Discussion: Project overview  Lecture slides, Discussion notes  HW3, HW3 solutions 
3/15  Spring break ☀️  
3/22  Lecture 9: Transformers; Decision trees; Ensemble methods Discussion: LLMs and foundation models  Lecture slides, Discussion notes  
3/29  Lecture 10: Boosting; Dimensionality reduction and visualization; PCA Discussion: HW3 review  Lecture slides, Discussion notes  HW4, HW4 solutions 
4/5  Lecture 11: Clustering; kmeans; Gaussian mixture models; EM Discussion: Evaluation metrics (precision, recall etc.)  Lecture slides, Discussion notes  
4/12  Lecture 12: Density estimation; Generative models & Naive Bayes; Multiarmed bandits Discussion: HW4 review  Lecture slides  Practice problems, Fall 2022 exam 
4/19  Lecture 13: Reinforcement Learning; Responsible ML, Fairness, Robustness, Privacy Discussion: Problem solving for Exam 2  Lecture slides  Practice problems (solutions) 
4/26  Lecture: Exam 2 📝 No Discussion session  
5/6  Project report due 📕 
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, exams 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.