DSC 345 Machine Learning

Casey Bennett

Office: CDM 373
Spring 2023-2024
Class number: 35345
Section number: 910
OLASY NCH00 Online Campus


This course focuses on the application of machine learning principles in Data Science and AI.  The course will cover advanced modeling techniques, including ensemble learning (bagging/boosting), neural networks, bayesian networks, biologically-inspired computing, feature selection and feature engineering techniques, Markov models for temporal modeling to find patterns over time, extended linear models & kernel methods (support vector machines), and clustering techniques.  We will also cover use of various packages like Scikit, Spark, XgBoost, and Keras.  The course will focus on both the theoretical aspects of the techniques and connections to natural intelligence.  State-of-the-art research will be explored through scientific paper reviews that highlight the use of advanced machine learning techniques to solve real world problems in medicine, finance, public policy, engineering, etc.  Students will learn through hands-on problem-based learning and discussion.

Course Learning Goals:

  • understand the mechanics behind each machine learning method, as well as the respective pros and cons, for solving data science problems
  • understand how to implement machine learning models on real world data using tools such as Python, Scikit-learn, Spark, XgBoost, and Keras
  • understand methods to evaluate the performance of machine learning models
  • understand how “information” in real world applications can be formulated as different data structures, and the effects of feature selection and other preprocessing techniques
  • develop a sophisticated understanding of how to interpret and explain obtained ML results to diverse audiences
  • understand the roots of various forms of machine learning from natural intelligence and human cognition, and scientific endeavors to create “artificial” intelligence

School policies:

Changes to Syllabus

This syllabus is subject to change as necessary during the quarter. If a change occurs, it will be thoroughly addressed during class, posted under Announcements in D2L and sent via email.

Online Course Evaluations

Evaluations are a way for students to provide valuable feedback regarding their instructor and the course. Detailed feedback will enable the instructor to continuously tailor teaching methods and course content to meet the learning goals of the course and the academic needs of the students. They are a requirement of the course and are key to continue to provide you with the highest quality of teaching. The evaluations are anonymous; the instructor and administration do not track who entered what responses. A program is used to check if the student completed the evaluations, but the evaluation is completely separate from the student’s identity. Since 100% participation is our goal, students are sent periodic reminders over three weeks. Students do not receive reminders once they complete the evaluation. Students complete the evaluation online in CampusConnect.

Academic Integrity and Plagiarism

This course will be subject to the university's academic integrity policy. More information can be found at If you have any questions be sure to consult with your professor.

All students are expected to abide by the University's Academic Integrity Policy which prohibits cheating and other misconduct in student coursework. Publicly sharing or posting online any prior or current materials from this course (including exam questions or answers), is considered to be providing unauthorized assistance prohibited by the policy. Both students who share/post and students who access or use such materials are considered to be cheating under the Policy and will be subject to sanctions for violations of Academic Integrity.

Academic Policies

All students are required to manage their class schedules each term in accordance with the deadlines for enrolling and withdrawing as indicated in the University Academic Calendar. Information on enrollment, withdrawal, grading and incompletes can be found at

Students with Disabilities

Students who feel they may need an accommodation based on the impact of a disability should contact the instructor privately to discuss their specific needs. All discussions will remain confidential.
To ensure that you receive the most appropriate accommodation based on your needs, contact the instructor as early as possible in the quarter (preferably within the first week of class), and make sure that you have contacted the Center for Students with Disabilities (CSD) at:
Lewis Center 1420, 25 East Jackson Blvd.
Phone number: (312)362-8002
Fax: (312)362-6544
TTY: (773)325.7296