CSCI 479: Fall 2026
Course Schedule

Dates and topics are approximate and are subject to change.
 
Dates Topics Labs Assignments/Quizzes Exams
Week 1 Introduction - History and Overview(Sept. 9) - - -
Week 2 Introduction - AI and Learning(Sept. 14)
Data(Sept. 16)		 Lab 1 - Data preprocessing A1 - Data Exploration and Report -
Week 3 Information based learning I(Sept. 21)
Information based learning II(Sept. 23)		 Lab 2 - Discretization algorithm - -
Week 4 Information based learning III(Sept. 28) Lab 3 - Decision Tree A2 - Decision Tree Application -
Week 5 Similarity based learning I(Oct. 5)
Similarity based learning II(Oct. 7)		 Lab 4 - Similarity Based predictive model - -
Week 6 Probability based learning I(Oct. 13)
Probability based learning II(Oct. 14)		 Lab 5 - Naive Bayes Classifier, Midterm Q/A A3 - Probability applications -
Week 7 Midterm (Oct. 19)
Error based learning I(Oct. 21)		 Lab 6 - Bayesian Belief Network Midterm -
Week 8 Error based learning II(Oct. 26)
Deep Learning(Oct. 28)		 Lab 7 - Linear Regression Model A4 - Regression model -
Week 9 Error based learning III(Nov. 2)
Predictive Model Evaluation(Nov. 4)		 Lab 8 - Neural network algorithm - -
- November 9-13
Fall Study Week		 - - -
Week 10 Predictive Model Evaluation(Nov. 16)
Cluster Analysis I(Nov. 18)		 Lab 9 - Model Evaluation A5 - Model and Evaluation -
Week 11 Cluster Analysis II(Nov. 23)
Outliers(Nov. 25)
Lab A - Clustering - -
Week 12 AI Agents I(Nov. 30)
AI Agents II(Dec. 2)		 Lab B - Agent Programming - -
Week 13 AI Agents III(Dec. 7)
Summary/Final Review(Dec. 9)		 (Optional) Presentation - -
Last updated: October 8, 2026