Pennsylvania State University

Course Information

DS 310: Machine Learning

Course Objectives

Upon completion of this course, students will be able to:

• Demonstrate broad understanding of the principles of machine learning and of representative machine learning algorithms and their applications in data sciences.
• Implement, adapt, and apply representatative machine learning algorithms using a high-level programming language, e.g., Python, to perform real-world clustering, classification, and regression tasks
• Identify, formulate and solve exploratory data analysis and predictive modeling problems that arise in practical applications.
• Demonstrate an understanding of the strengths and weaknesses of alternative machine learning algorithms (relative to the characteristics of the application domain)
• Adapt or combine some of the key elements of existing machine learning algorithms to design new algorithms as needed.
• Rigorously evaluate or compare alternative machine learning algorithms on particular problems
• Apply best practices in responsible data science (covering issues such as reproducibility, and bias).
• Effectively communicate the results of machine learning projects to technical and non-technical audiences

Course Schedule

Please note that the precise schedule is subject to change. Detailed study guide, including lecture slides, assigned readings can be found here. The problem sets, lab assignments, and projects are posted on Canvas.

• Lecture 1. Learning from data: motivations, representative applications.
• Lecture 2. Review of python for machine learning. Simple Examples of Machine Learning: Nearest Neighbor Classifier, Nearest Neighbor function approximation.
• Lecture 3: Linear Regression: Review of multi-variable calculus, gradient descent. Locally weighted linear regression.
• Lecture 4: Data Representation: Feature Extraction, Feature Selection, Dimensionality Reduction
• Lecture 5: Evaluation of Machine Learning Algorithms (and predictive models).
• Lecture 6: Basic linear classifiers (perceptrons)
• Lecture 7: Basic linear classifiers (winner-take-all, winnow)
• Lecture 8: Decision Trees
• Lecture 9: Probabilistic Generative Models. Bayes Optimal Classifier
• Lecture 10 Naive Bayes Classifier, Probability Estimation
• Lecture 11: Discriminative Models. Classification via Regression.
• Lecture 12: Logistic Regression and Regularized Logistic Regression.
• Lecture 13: Mid Term Review
• Mid-term Exam
• Lecture 15: Classifiers based on the Exponential Family
• Lecture 16: Multi-Class Generative and Discriminative Models
• Lecture 17: Kernel Machines: Support Vector Machines
• Lecture 18: Kernel Machines: Kernel Logistic Regression
• Lecture 19: Kernel Machines: Kernel Design
• Lecture 20: Ensemble Methods: Adaboost, Random Forests
• Lecture 21: Deep Learning: Multi-layer Neural Networks and Backpropagation algorithm
• Lecture 22: Representation Learning: Autoencoders and Deep Neural Networks
• Lecture 23: Clustering: K-means, hierarchical and related methods
• Lecture 24: Mixture Models and Expectation Maximization
• Lecture 25: Spectral Clustering
• Lecture 26: Bias and Fairness in Machine Learning
• Lecture 27: Synthesis: Important ideas in machine learning
• Lecture 28: Review
• Final Exam

Course Materials

• Texts and References
• Study Guide
• Course Assignments: Course assignments will be posted on Canvas.
• Machine Learning Resources

Help

• IST peer tutoring program provides a space for students to enhance their knowledge of course topics in an engaging setting. Group Tutoring sessions are held on Wednesdays and Thursdays from 7:00 to 10:00pm in Westgate E203. Tutoring sessions occur in an individual or small group setting that is led by a student with expertise in that course. Tutors will aid students by clarifying course concepts rather than re-teaching course material. Peer tutoring is available for 100 and 200 level courses taught within The College of IST. Many higher-level courses are also supported. Tutoring begins on the week of August 28th, 2022. For additional information, visit the tutoring services.

Counseling Services

Many students at Penn State face personal challenges or have psychological needs that may interfere with their academic progress, social development, or emotional wellbeing. The university offers a variety of confidential services to help you through difficult times, including individual and group counseling, crisis intervention, consultations, online chats, and mental health screenings. These services are provided by staff who welcome all students and embrace a philosophy respectful of clients’ cultural and religious backgrounds, and sensitive to differences in race, ability, gender identity and sexual orientation.

• Counseling and Psychological Services, 814-863-0395
• Penn State Crisis Line (24 hours/7 days/week): 877-229-6400 Crisis Text Line (24 hours/7 days/week): Text LIONS to 741741

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