Course Landing Page

Topics to be covered:

  1. Unit1: Same as Current Structure (Use Chapter2: 2.1 - 2.2 and 2.4)
  2. Unit2: Refresher on Linear Regression & Resampling methods (Use IMS 2e: CH7,8 | Use Chapters3, 5 ISLR)
  3. Unit3: Tree based methods (Use Chapter8 ISLR)
  4. Unit4: Same as Unit2 of the Current Structure
  5. Unit5: Sentiment analysis

If we have more time we may also cover one or more of:

  1. RDD
  2. RCT
  3. IV

Lecture Slides

  1. Lecture Slides: Intro to Statistical Learning.
  2. Lecture Slides: Intro to Modern Statistics
  3. Lecture Slides: Resampling
  4. Lecture Slides: Decision Trees and Ensemble Methods
  5. Lecture Slides: Text Analysis - An Introduction
  6. Slides: Self Assessmentv-Fundamentals of exploratory and descriptive analysis

Assessment Details

Type # ILO linkages Assessment Percentage Details
Quiz 1 15 Intro to statistical learning and Linear Regression
inclass exam 1 30 LR, resampling, decision trees
inclass exam 2 30 PCA, sentiment analysis, Ethics of data
CP 3 5
GRP Prjoect 2 20 apply machine methods learned in class to real data and present their findings.

May change with adequate notice.

Links to resourses:

  1. ISLR, also check out the youtube videos they have. Use the june 2023 corrected version of the book
  2. IMS
  3. For a simulated and intuitive explanation of Bias-Variance Trade off see Link
  4. Text Mining with R