The Reading Room

Deep dive into expressions and statement-level control: how expressions are evaluated, assignment and type conversions, short-circuit evaluation, and the selection, loop, and jump statements that direct execution.

Sources:

Week 8 (Oct 26-29) – Sebesta §§7.2–7.8: Expressions and Assignment

Week 8 (Oct 26-29) – Sebesta §§8.1–8.4: Statement-Level Control Structures

Deep dive into model evaluation and ensemble methods: how to measure and improve models, and how bagging, random forests, boosting, and Bayesian additive regression trees combine weak learners into stronger ones.

Sources:

Week 7 (Oct 26-28) – ISLP Ch 8.2: Bagging, Random Forests, Boosting, BART

Week 7 (Oct 26-28) – Muller & Guido Ch 5: Model Evaluation and Improvement

Test-prep deep dive for the closed-book Test 2: language implementation methods, BNF/EBNF syntax description, parse trees and ambiguity, operational semantics of assignment, lexical and syntax analysis, names, binding, scope and lifetime, plus pointer/reference types and type checking — built from the lecture slides and textbook readings since Test 1.

Sources:

Test 2 prep – Slides: Week 5 Lecture 2 (Oct 8)

Test 2 prep – Slides: Week 6 Lecture 1 (Oct 12)

Test 2 prep – Slides: Week 7 Names, Scope, Binding (Oct 19)

Test 2 prep – Sebesta §1.7: Implementation Methods

Test 2 prep – Sebesta §§3.1–3.2: Describing Syntax

Test 2 prep – Sebesta §3.3: Formal Methods of Describing Syntax

Test 2 prep – Sebesta §3.5: Assignment Example (Semantics)

Test 2 prep – Sebesta §4.1: Lexical and Syntax Analysis Intro

Test 2 prep – Sebesta §§4.2–4.4: Lexical and Syntax Analysis

Test 2 prep – Sebesta §§5.2–5.5, 5.7: Names, Bindings, Scopes

Test 2 prep – Sebesta §§5.4–5.6: Binding, Scope, Lifetime

Test 2 prep – Sebesta §§6.11–6.12: Pointer/Reference Types, Type Checking

Soft skills in the post-AI workplace: what engineering students think soft-skills education is worth, how soft skills fit 21st-century project management, the NACE career-readiness competencies, and the human element in Industry 5.0 projects — plus the dark side of the digital workplace and the foundations of emotional intelligence.

Sources:

Class 13–14 (Oct 20-22) – Balberg et al. (2025): Soft skills education

Class 13–14 (Oct 20-22) – Kearney et al. (2024): Soft skills for project management

Class 13–14 (Oct 20-22) – Santos et al. (2025): Human element in Industry 5.0

Class 13–14 (Oct 20-22) – Salovey & Mayer (1990): Emotional intelligence

Class 13–14 (Oct 20-22) – NACE (2024): Career Readiness Competencies

Class 13–14 (Oct 20-22) – Marsh et al. (2022): Digital workplace dark side

A recap of binding, scope, and lifetime, then pointer and reference types plus how type checking works — closing with the fundamentals of functional programming, from mathematical functions to what makes a language functional.

Sources:

Week 7 (Oct 19-22) – Sebesta §§5.4–5.6: Binding, Scope, Lifetime

Week 7 (Oct 19-22) – Sebesta §§6.11–6.12: Pointer/Reference Types, Type Checking

Week 7 (Oct 19-22) – Sebesta §§15.1–15.3: Functional Programming Fundamentals

Representing data so algorithms can learn from it: categorical variables and one-hot encoding, binning and scaling continuous features, and engineering new features that capture what matters.

Sources:

Week 6 (Oct 19-21) – Müller & Guido Ch. 4: Representing Data and Engineering Features

How compilers turn source code into structured logic: formal grammars and BNF/EBNF notation, parse trees and ambiguity, how lexical analysis breaks code into tokens, the parsing problem and recursive-descent parsing — plus names, variables, binding, scope, and referencing environments.

Sources:

Week 6 (Oct 12-15) – Sebesta §3.3: Formal Methods of Describing Syntax

Week 6 (Oct 12-15) – Sebesta §§4.2–4.4: Lexical and Syntax Analysis

Week 6 (Oct 12-15) – Sebesta §§5.2–5.5, 5.7: Names, Bindings, Scopes

A 49-minute deep-dive study guide for the October 14 closed-book ML exam, covering weeks 1–4: the ingredients of machine learning, statistical learning, logical tree-based models, probabilistic models (logistic regression, Naive Bayes), geometric models (k-nearest neighbors, support vector machines), and linear regression — plus a walkthrough of the Fall 2026 practice exam. Theory, model trade-offs, evaluation metrics, and exam-style calculations; no programming.

Sources:

Practice exam – Fall 2026 (from Canvas).pdf

Week 1 (Sep 7+9) – Reading – Flach 1.1-1.2, The ingredients of ML.pdf

Week 1 (Sep 7+9) – Reading – Flach book slides.pdf

Week 1 (Sep 7+9) – Reading – ISLP Ch 1, Introduction.pdf

Week 1 (Sep 7+9) – Slides – The very intro to ML.pdf

Week 1-2 (Sep 7-23) – Reading – ISLP Ch 2, Statistical learning.pdf

Week 2 (Sep 21+23) – Reading – ISLP 8.1-8.2, Tree-based methods.pdf

Week 2 (Sep 21+23) – Slides – Classification, logical models.pdf

Week 2 (Sep 21+23) – Slides – Logical tree models, performance.pdf

Week 3 (Sep 30) – Reading – ISLP 2.2.3, K-nearest neighbors.pdf

Week 3 (Sep 30) – Reading – ISLP 4.1 and 4.3, Classification and logistic regression.pdf

Week 3 (Sep 30) – Reading – ISLP 4.4.4, Naive Bayes.pdf

Week 3 (Sep 30) – Reading – ISLP 4.7.6, KNN lab.pdf

Week 3 (Sep 30) – Reading – ISLP 9.1-9.3, Support vector machines.pdf

Week 3 (Sep 30) – Slides – Probabilistic and geometric models.pdf

Week 4 (Oct 5+7) – Reading – ISLP Ch 3, Linear regression.pdf

Week 4 (Oct 5+7) – Slides – Dataset checklist framework.pdf

Week 4 (Oct 5+7) – Slides – Regression.pdf

Week 4 (Oct 5+7) – Slides – Understanding data.pdf

How does source code become a running program? This episode follows the full pipeline: implementation methods — compilers, interpreters, and hybrids (1.7); describing syntax with grammars, parse trees, and ambiguity (3.1–3.2); the key ideas of lexical and syntax analysis (4.1); and what "meaning" looks like at runtime, through an assignment statement in operational semantics (3.5).

Sources:

Week 5 (Oct 5-11) – 1.7 Implementation Methods

Week 5 (Oct 5-11) – 3.1-3.2 Describing Syntax

Week 5 (Oct 5-11) – 4.1 Intro to Lexical and Syntax Analysis

Week 5 (Oct 5-11) – 3.5 Assignment Example (Operational Semantics)

Week 5 (Oct 5-11) – What Is GDPR Compliance? (Palo Alto Networks)

Week 5 (Oct 5-11) – NIST Privacy Framework Getting Started