Merge branch 'main' of https://github.com/UBC-CS/cpsc330-2024W1

UBC-CS · Nov 19, 2024 · 7094dec · 7094dec
2 parents 9dfec97 + 93a205a
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diff --git a/README.md b/README.md
@@ -68,7 +68,7 @@ Usually the homework assignments will be due on Mondays (except next week) and w
 | hw6            | ~~November 04~~ Nov 6th, 11:59 pm | [GitHub repo](https://github.com/new?template_name=hw6&template_owner=ubc-cpsc330) | [Gradescope](https://www.gradescope.ca/courses/18608) |
 | **Midterm 2**  | Nov 14 and Nov 15     | PrairieLearn (CBTF, in person)                                                     | PrairieLearn (CBTF, in person)                        |
 | hw7            | November 18, 11:59 pm | [GitHub repo](https://github.com/new?template_name=hw7&template_owner=ubc-cpsc330) | [Gradescope](https://www.gradescope.ca/courses/18608) |
-| hw8            | November 25, 11:59 pm | GitHub repo[](https://github.com/new?template_name=hw8&template_owner=ubc-cpsc330) | [Gradescope](https://www.gradescope.ca/courses/18608) |
+| hw8            | November 25, 11:59 pm | [GitHub repo(https://github.com/new?template_name=hw8&template_owner=ubc-cpsc330) | [Gradescope](https://www.gradescope.ca/courses/18608) |
 | hw9            | December 05, 11:59 pm | GitHub repo[](https://github.com/new?template_name=hw9&template_owner=ubc-cpsc330) | [Gradescope](https://www.gradescope.ca/courses/18608) |
 | **Final exam** | TBA                   | PrairieLearn (CBTF, in person)                                                           | PrairieLearn (CBTF, in person)                              |
 

diff --git a/_toc.yml b/_toc.yml
@@ -37,9 +37,9 @@ parts:
   - file: lectures/notes/16_DBSCAN-hierarchical.ipynb  
   - file: lectures/notes/17_recommender-systems.ipynb
   - file: lectures/notes/18_natural-language-processing.ipynb
-  - file: lectures/notes/19_intro_to_computer_vision.ipynb
-  - file: lecture/notes/20_time-series.ipynb
-  - file: lectures/notes/appendixA_feature-engineering.ipynb
+  - file: lectures/notes/19_intro_to_computer-vision.ipynb
+  - file: lectures/notes/20_time-series.ipynb
+  - file: lectures/notes/appendixA_feature-engineering-text-data.ipynb
   - file: lectures/notes/appendixB_multiclass-strategies.ipynb
 - caption: Section slides
   chapters:
@@ -94,6 +94,22 @@ parts:
         title: Lecture 15                
       - url: "https://kvarada.github.io/cpsc330-slides/lecture-16.html"
         title: Lecture 16                        
+      - url: "https://kvarada.github.io/cpsc330-slides/lecture-17.html"
+        title: Lecture 17                        
+      - url: "https://kvarada.github.io/cpsc330-slides/lecture-18.html"
+        title: Lecture 18                        
+      - url: "https://kvarada.github.io/cpsc330-slides/lecture-19.html"
+        title: Lecture 19                        
+    # - url: "https://kvarada.github.io/cpsc330-slides/lecture-20.html"
+    #   title: Lecture 20                        
+    # - url: "https://kvarada.github.io/cpsc330-slides/lecture-21.html"
+    #   title: Lecture 21                        
+    # - url: "https://kvarada.github.io/cpsc330-slides/lecture-22.html"
+    #   title: Lecture 22                        
+    # - url: "https://kvarada.github.io/cpsc330-slides/lecture-23.html"
+    #   title: Lecture 23                        
+    # - url: "https://kvarada.github.io/cpsc330-slides/lecture-24.html"
+    #   title: Lecture 24                                
   - file: lectures/103-Firas-lectures/README
     sections:
       - url: "https://firasm.github.io/cpsc330-slides/slides-01-intro"

diff --git a/lectures/notes/19_intro_to_computer-vision.ipynb b/lectures/notes/19_intro_to_computer-vision.ipynb
@@ -156,7 +156,15 @@
     "- If they don't a common technique is to reduce multiclass classication into several instances of binary classification problems. \n",
     "- Two kind of \"hacky\" ways to reduce multi-class classification into binary classification:  \n",
     "    - the one-vs.-rest approach\n",
-    "    - the one-vs.-one approach "
+    "    - the one-vs.-one approach\n",
+    "- Check out [appendixB](appendixB_multiclass-classification.html) for more details. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at a multiclass classification with logistic regression. "
    ]
   },
   {
@@ -808,17 +816,6 @@
     "lr.predict(X_test)[0]"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For models such as SVMs which do not naturally extend to multiclass classification, a common technique is to reduce multiclass classication into several instances of binary classification problems. \n",
-    "- Two kind of \"hacky\" ways to reduce multi-class classification into binary classification:  \n",
-    "    - the one-vs.-rest approach\n",
-    "    - the one-vs.-one approach\n",
-    "- Check out [appendixB](appendixB_multiclass-classification.html) for more details. "
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},

diff --git a/lectures/notes/20_time-series.ipynb b/lectures/notes/20_time-series.ipynb
@@ -19,7 +19,7 @@
     }
    },
    "source": [
-    "# Lecture 19: Time series\n",
+    "# Lecture 20: Time series\n",
     "\n",
     "UBC 2024-25"
    ]