ADAN 7430
ML/AI Algorithms I · 4 instructors on record · 21 sections
That is the class itself, averaged across everyone who taught it. Who taught it, and how each of them scored, is the board below — that is what a student picking a section is actually choosing between.
Boston College's own “intellectually challenging” question for this class only — students answer it positively about courses that stretched them, so it measures intellectual challenge and NOT grading, workload or quality. Almost every BC class scores high on it, which is why it is shown as a rank rather than a score.
Boston College reports workload for this class only as one of five bands, not as a number of hours, and it does not publish what the bands mean — so this site will not translate it into hours. Higher means more work, which is not the same as worse.
This course demonstrates how to merge economic data analysis and applied econometric tools with the most common machine learning techniques, as the rapid advancement of computational methods provides unprecedented opportunities for understanding big data. This course will provide a hands-on experience with the terminology, technology and methodologies behind machine learning with economic applications in marketing, finance, healthcare, and other areas. The main topics covered in this course include: advanced regression techniques, resampling methods, model selection and regularization, classification models (logistic regression, Naive Bayes, discriminant analysis, k-nearest neighbors, neural networks), tree-based methods, support vector machines, and unsupervised learning (principal components analysis and clustering). Students will apply both supervised and unsupervised machine learning techniques to solve various economics-related problems with real-world data sets. No prior experience with R or Python is necessary.
Scores are real averages. The order also accounts for how many records back each one — the count is under every score — so one perfect review does not outrank a semester of them.
| Rank | Instructor | Overall | Workload | Challenge |
|---|---|---|---|---|
| 1 | Arvind SharmaAdvancing Studies | 935 records | 93rd | 76th |
| 2 | Stefano ParravanoAdvancing Studies | 914 records | 93rd | 79th |
| 3 | Razvan VelicheAdvancing Studies | 778 records | 94th | 63rd |
| 4 | Ashuraj SirohiAdvancing Studies | 754 records | 95th | 40th |
How this board is orderedPositions account for how many records stand behind each number, so one perfect review does not outrank a semester of them. Every score shown is the real average — only the ordering is adjusted. The comparison here is the class itself: a thin record is read against how this class was rated overall, not against the university.
Every instructor with an evaluation record for this class is on this board — it is not a top ten, and nothing is hidden behind a “show more”. Overall is the instructor's blended score for THIS class only, and its one contributing source is Boston College's evaluations for the sections in the row's record count — nothing on this board is an average across the other things they teach. Workload and Challenge are RANKS against every class we hold, not scores: BC's questions all sit near the top of the 1-5 scale, so a raw number would put almost every row in the high eighties and tell you nothing. Challenge is BC's “intellectually challenging” item, which students answer positively about courses that stretched them — it is not a measure of difficulty or grading. Neither column is ever marked better or worse.
The line
All 13 items · means across 21 recordsBar length is the rank against every comparable record we hold, not the raw score: BC's questions all have a median around 4.5, so a raw-scale bar would be nearly full on every row and would tell you nothing. Every bar is one ink colour — nothing here is graded green or red. The hatched bars are not ratings at all: a higher number on either describes a different course, not a better one.
Game log
21 sections · newest first| Instructor | Term | Sec | Instr | Course | Work |
|---|---|---|---|---|---|
| Arvind Sharma | Spring 2026 | 01 | 100 | 97 | 80 |
| Arvind Sharma | Spring 2026 | 02 | 95 | 80 | 80 |
| Arvind Sharma | Fall 2025 | 01 | 88 | 90 | 80 |
| Stefano Parravano | Fall 2025 | 02 | 80 | 70 | — |
| Razvan Veliche | Summer 2025 | 01 | 87 | 87 | 100 |
| Stefano Parravano | Summer 2025 | 02 | 100 | 100 | 80 |
| Arvind Sharma | Spring 2025 | 01 | 97 | 91 | 80 |
| Razvan Veliche | Spring 2025 | 02 | 70 | 70 | 80 |
| Arvind Sharma | Fall 2024 | 01 | 87 | 87 | 80 |
| Razvan Veliche | Fall 2024 | 03 | 92 | 92 | 80 |
| Razvan Veliche | Summer 2024 | 01 | 67 | 60 | 100 |
| Stefano Parravano | Summer 2024 | 02 | 95 | 95 | — |
| Razvan Veliche | Spring 2024 | 01 | 72 | 78 | 60 |
| Ashuraj Sirohi | Spring 2024 | 02 | 53 | 70 | — |
| Razvan Veliche | Fall 2023 | 01 | 69 | 69 | — |
| Stefano Parravano | Summer 2023 | 02 | 90 | 80 | — |
| Ashuraj Sirohi | Spring 2023 | 01 | 75 | 75 | — |
| Ashuraj Sirohi | Fall 2022 | 01 | 80 | 80 | 100 |
| Ashuraj Sirohi | Midterm Fall 2023 | 02 | 90 | 80 | 80 |
| Razvan Veliche | Midterm Fall 2022 | 02 | 100 | 80 | 80 |
| Razvan Veliche | Spring 1 2023 | 02 | 60 | 60 | — |
How to read this boardInstructors are ranked on their record in this class only, never on their average across everything they teach. Scores are out of 100 — Boston College's 1–5 agreement scale, ×20, the way these numbers were printed for years. Workload is BC's own five-band figure, not a number of hours, and is shown as a rank. A dash means the question was not reported, and is never a zero.