ITE 401 Syllabus

Course Description

This course introduces students to machine learning concepts and Python applications. Topics include data acquisition, data modeling, supervised and unsupervised learning, reinforcement learning, neural networks, and deep learning. (3 credits)

Prerequisite

ITE 301: Introduction to AI and Generative AI

Student Learning Outcomes (SLOs)

Students who successfully complete this course will be able to:

Describe features of machine learning (ML) and deep learning (DL), discuss differences between ML and DL, and classify applications of ML and DL.
Discuss the reasons behind the accelerated growth of ML and DL applications.
Explore and use a variety of Python data structures and libraries to solve problems.
Discuss mathematical concepts that underly artificial intelligence (AI) models and implement mathematical concepts related to AI using Python.
Discuss the role of dimensionality reduction in ML algorithms and implement methods to perform dimensionality reduction.
Perform statistical data analysis, implement ML algorithms, and create data visualizations using no-code data analysis and ML tools.
Describe the differences between supervised, unsupervised, and reinforcement learning models.
Describe and use common metrics for evaluating regression and classification ML models.
Discuss the role and importance of tuning ML models to improve model performance.
Describe the underlying mathematical concepts used in ML algorithms, including linear regression, logistic regression, k-nearest neighbor (KNN), support-vector machine (SVM), and decision trees.
Explain the working of clustering algorithms and discuss various applications of clustering algorithms and unsupervised learning ML models.
Explain the working of a recommendation system and discuss various applications of recommendation systems.
Explain the working of reinforcement learning models and discuss various applications of reinforcement learning models.
Explain the working of a neural network and discuss the similarities of a neural network neuron and a biological neuron.
Explain the mathematics behind the working of a simple neural network and discuss various applications of neural networks.
Discuss the importance of variance and bias in DL models and describe methods to overcome variance and bias in DL models.
Implement supervised learning ML models, unsupervised learning ML models, and DL models within an AI project cycle to solve statistical data and machine learning problems.
Describe current trends and emerging technologies in ML and ML applications.

Course Activities and Grading

Assignments	Weight
Discussions (Weeks 1-8)	20%
Python Coding Activities (Weeks 1, 2, 3, 5 & 7)	30%
Projects (Weeks 2, 4, 6 & 8)	30%
Midterm Project (Week 4)	10%
Final Exam (Week 8)	10%
Total	100%

Required Textbook

This course uses Open Educational Resources (OER). OER are openly licensed, educational resources that can be used for teaching, learning and research. OER may consist of a variety of resources such as textbooks, videos and software that are no cost for students.

Course Schedule

Week	SLOs	Readings and Exercises	Assignments
1	1-4	Topic: Exploring Machine Learning, Deep Learning, and AI with Python	Review the lecture material Review Instructions for Completing Python Assignments Participate in the Discussions Week 1 Coding Activity - AI Mathematics in Python
2	5,6	Topic: Dimensionality Reduction, No-Code Data Analysis, and ML Tools	Review the lecture material Participate in the Discussions Submit Week 2 Coding Activity - Dimensionality Reduction using MNIST Data Set Submit Week 2 Project - Data Exploration Using Orange Data Mining
3	7-10	Topic: Supervised Learning ML Models	Review assigned links Review the lecture material Participate in the Discussions Submit Week 3 Coding Activity - Supervised Learning Models in Python
4	11-13,17	Topic: Unsupervised Learning ML Models, Clustering Algorithms, and Recommendation Systems	Review assigned links Review the lecture material Participate in the Discussions Submit Week 4 Project - Implementing a Recommendation System in Python Submit Week 4 Midterm Exam
5	14,15,17	Topic: Neural Networks and Deep Learning Algorithms	Review assigned links Review the lecture material Participate in the Discussions Submit Week 5 Coding Activity - Implementing Neural Networks in Python
6	16,17	Topic: Variance and Bias in ML Models	Review assigned links Review the lecture material Participate in the Discussions Submit Week 6 Project - Implementing Supervised Learning Models within an AI Project Cycle
7	17	Topic: Unsupervised Learning and Deep Learning in AI Project Cycles	Review the lecture material Participate in the Discussions Submit Week 7 Coding Activity - Implementing Supervised and Unsupervised Learning Models within AI Project Cycles
8	17,18	Topic: Current Trends in ML Applications and Emerging ML Technologies	Review the lecture material Participate in the Discussions Submit Week 8 Project - Implementing Deep Learning Models within an AI Project Cycle Submit Week 8 Final Exam Complete Course Evaluation

COSC Accessibility Statement

Charter Oak State College encourages students with disabilities, including non-visible disabilities such as chronic diseases, learning disabilities, head injury, attention deficit/hyperactive disorder, or psychiatric disabilities, to discuss appropriate accommodations with the Office of Accessibility Services at OAS@charteroak.edu.

COSC Policies, Course Policies, Academic Support Services and Resources

Students are responsible for knowing all Charter Oak State College (COSC) institutional policies, course-specific policies, procedures, and available academic support services and resources. Please see COSC Policies for COSC institutional policies, and see also specific policies related to this course. See COSC Resources for information regarding available academic support services and resources.

ITE 401: Introduction to Machine Learning