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DS 310 [Fall 2023] Machine Learning for Data Analytics

DS 310: Machine Learning for Data Analytics

Overview

The course teaches students the principles of machine learning (and data mining) and their applications in the data sciences. The course introduces the principles of machine learning (and data mining), representative machine learning algorithms and their applications to real-world problems. Topics to be covered include: principled approaches to clustering, classification, and function approximation from data, feature selection and dimensionality reduction, assessing the performance of alternative models, and relative strengths and weaknesses of alternative approaches. The course will include a laboratory component to provide students with hands-on experience with applications of the algorithms to problems from several domains. Prerequisites for the course include basic proficiency in programming, elementary probability theory and statistics, and discrete mathematics.

Logistics

Instructor Team

Email : Use Canvas email - all course-related email, including messages to your instructor, TA and fellow students should be sent within Canvas, using the Inbox.

Course Materials

Grading Policy

Grades will be computed based on the following factors:

Final grade cutoff:

Schedule

# Date Topics Assigned Due
1 08/22/23 Course Introduction    
2 08/24/23 ML Fundamentals    
3 08/29/23 Hands-on Lab 1: Python, Pandas, and Numpy   Lab1
4 08/31/23 Regression: Linear Regression 1    
5 09/05/23 Regression: Linear Regression 2 HW1  
6 09/07/23 Regression: Linear Regression 3    
7 09/12/23 Regression: Linear Regression 4    
8 09/14/23 Hands-on Lab 2: Kaggle and Regression GP1 Lab2
9 09/19/23 Classification: Evaluation and K Nearest Neighbors   HW1
10 09/21/23 Classification: Logistic Regression and Perceptron HW2  
11 09/26/23 Hands-on Lab 3: Classification 1   Lab3
12 09/28/23 Classification: Decision Trees    
13 10/03/23 Classification: Naive Bayesian GP2 GP1
14 10/05/23 Classification: Ensemble Learning    
15 10/10/23 Midterm Review   HW2
16 10/12/23 Midterm    
17 10/17/23 Clustering: Basics    
18 10/19/23 Clustering: K-means Clustering   GP2
19 10/24/23 Clustering: Hierarchical & Density-based Clustering HW3  
20 10/26/23 Deep Learning: Introduction GP3  
21 10/31/23 Deep Learning: CNN & RNN    
22 11/02/23 Hands-on Lab 4: Clustering   Lab4
23 11/07/23 Hands-on Lab 5: PyTorch & CNN & RNN   Lab5
24 11/09/23 Deep Learning: Attention, Transformers, and LLM   HW3
25 11/14/23 Deep Learning: Adversarial Machine Learning 1    
26 11/16/23 Deep Learning: Adversarial Machine Learning 2    
– 11/21/23 Thanksgiving    
– 11/23/23 Thanksgiving    
27 11/28/23 Hands-on Lab 6: Adv ML   Lab6
28 11/30/23 Final Review   GP3
29 12/05/23 Group Project Expo    
30 12/07/23 Final Exam    

The instructor reserves the right to make any changes.

Late Submission Policy

ACADEMIC INTEGRITY STATEMENT

Academic integrity is the pursuit of scholarly activity in an open, honest and responsible manner. Academic integrity is a basic guiding principle for all academic activity at The Pennsylvania State University, and all members of the University community are expected to act in accordance with this principle. Consistent with this expectation, the University’s Code of Conduct states that all students should act with personal integrity, respect other students’ dignity, rights and property, and help create and maintain an environment in which all can succeed through the fruits of their efforts.

Academic integrity includes a commitment by all members of the University community not to engage in or tolerate acts of falsification, misrepresentation or deception. Such acts of dishonesty violate the fundamental ethical principles of the University community and compromise the worth of work completed by others.

DISABILITY ACCOMMODATION STATEMENT

Penn State welcomes students with disabilities into the University’s educational programs. Every Penn State campus has an office for students with disabilities. Student Disability Resources (SDR) website provides contact information for every Penn State campus (http://equity.psu.edu/sdr/disability-coordinator). For further information, please visit Student Disability Resources website (http://equity.psu.edu/sdr/).

In order to receive consideration for reasonable accommodations, you must contact the appropriate disability services office at the campus where you are officially enrolled, participate in an intake interview, and provide documentation: See documentation guidelines (http://equity.psu.edu/sdr/guidelines). If the documentation supports your request for reasonable accommodations, your campus disability services office will provide you with an accommodation letter. Please share this letter with your instructors and discuss the accommodations with them as early as possible. You must follow this process for every semester that you request accommodations.

COUNSELING AND PSYCHOLOGICAL SERVICES STATEMENT

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 at University Park (CAPS) (http://studentaffairs.psu.edu/counseling/): 814-863-0395

Counseling and Psychological Services at Commonwealth Campuses (https://senate.psu.edu/faculty/counseling-services-at-commonwealth-campuses/)

Penn State Crisis Line (24 hours/7 days/week): 877-229-6400 Crisis Text Line (24 hours/7 days/week): Text LIONS to 741741

EDUCATIONAL EQUITY/REPORT BIAS STATEMENTS

Consistent with University Policy AD29, students who believe they have experienced or observed a hate crime, an act of intolerance, discrimination, or harassment that occurs at Penn State are urged to report these incidents as outlined on the University’s Report Bias webpage (http://equity.psu.edu/reportbias/)