update enviroment for conda, fixed all test and updated readme

Author: abeljim

README.md

@@ -40,15 +40,20 @@ Features from Python 3.6 and 3.7 that we will be using for this version of the c
 
 ## Installation Guide
 
+First, make sure you have Python 3.7+ and conda installed:
+- [Install Python](https://www.python.org/downloads)
+- [Install conda](https://www.anaconda.com/docs/getting-started/miniconda/install) (Miniconda recommended)
+
 To download the repository:
 
-`git clone https://github.com/aimacode/aima-python.git`
+`git clone https://github.com/abeljim/aima-python-eecs118-fall-25.git`
 
-Then you need to install the basic dependencies to run the project on your system:
+Then you need to create a conda environment and install the dependencies:
 
 ```
-cd aima-python
-pip install -r requirements.txt
+cd aima-python-eecs118-fall-25
+conda env create -f environment.yml
+conda activate aima-python
 ```
 
 You also need to fetch the datasets from the [`aima-data`](https://github.com/aimacode/aima-data) repository:
@@ -58,11 +63,7 @@ git submodule init
 git submodule update
 ```
 
-Wait for the datasets to download, it may take a while. Once they are downloaded, you need to install `pytest`, so that you can run the test suite:
-
-`pip install pytest`
-
-Then to run the tests:
+Wait for the datasets to download, it may take a while. Then to run the tests:
 
 `py.test`
 

deep_learning4e.py

@@ -575,7 +575,7 @@ def AutoencoderLearner(inputs, encoding_size, epochs=200, verbose=False):
     model.add(Dense(input_size, activation='relu', kernel_initializer='random_uniform', bias_initializer='ones'))
 
     # update model with sgd
-    sgd = optimizers.SGD(lr=0.01)
+    sgd = optimizers.SGD(learning_rate=0.01)
     model.compile(loss='mean_squared_error', optimizer=sgd, metrics=['accuracy'])
 
     # train the model

environment.yml

@@ -0,0 +1,23 @@
+name: aima-python
+channels:
+  - defaults
+dependencies:
+  - python=3.9
+  - numpy
+  - matplotlib
+  - pandas
+  - scipy
+  - jupyter
+  - ipython
+  - pytest
+  - pillow
+  - pip
+  - pip:
+    - networkx
+    - opencv-python
+    - ipythonblocks
+    - sortedcontainers
+    - qpsolvers
+    - cvxopt
+    - keras
+    - tensorflow